diff --git a/poetry.lock b/poetry.lock
index 76c7ccfb0..32df8fe24 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,14 +1,14 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "anyio"
-version = "4.2.0"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
-    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
 ]
 
 [package.dependencies]
@@ -22,13 +22,13 @@ trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "appnope"
-version = "0.1.3"
+version = "0.1.4"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 files = [
-    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
-    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
 [[package]]
@@ -171,19 +171,22 @@ dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.2"
+version = "4.12.3"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
-    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
+    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
+    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
@@ -207,13 +210,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "certifi"
-version = "2023.11.17"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
-    {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
 ]
 
 [[package]]
@@ -406,13 +409,13 @@ files = [
 
 [[package]]
 name = "comm"
-version = "0.2.1"
+version = "0.2.2"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "comm-0.2.1.tar.gz", hash = "sha256:0bc91edae1344d39d3661dcbc36937181fdaddb304790458f8b044dbc064b89a"},
+    {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"},
+    {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
 ]
 
 [package.dependencies]
@@ -486,63 +489,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "coverage"
-version = "7.4.0"
+version = "7.4.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
 interpolatable = ["munkres", "pycairo", "scipy"]
-lxml = ["lxml (>=4.0,<5)"]
+lxml = ["lxml (>=4.0)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
 repacker = ["uharfbuzz (>=0.23.0)"]
@@ -745,13 +752,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.12.2"
+version = "2024.3.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -769,7 +776,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -797,18 +804,74 @@ dev = ["flake8", "markdown", "twine", "wheel"]
 
 [[package]]
 name = "griffe"
-version = "0.38.1"
+version = "0.42.1"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 colorama = ">=0.4"
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.5"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
+    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<0.26.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.27.0"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
+    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+
 [[package]]
 name = "idna"
 version = "3.6"
@@ -822,13 +885,13 @@ files = [
 
 [[package]]
 name = "imageio"
-version = "2.33.1"
+version = "2.34.0"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "imageio-2.33.1-py3-none-any.whl", hash = "sha256:c5094c48ccf6b2e6da8b4061cd95e1209380afafcbeae4a4e280938cce227e1d"},
-    {file = "imageio-2.33.1.tar.gz", hash = "sha256:78722d40b137bd98f5ec7312119f8aea9ad2049f76f434748eb306b6937cc1ce"},
+    {file = "imageio-2.34.0-py3-none-any.whl", hash = "sha256:08082bf47ccb54843d9c73fe9fc8f3a88c72452ab676b58aca74f36167e8ccba"},
+    {file = "imageio-2.34.0.tar.gz", hash = "sha256:ae9732e10acf807a22c389aef193f42215718e16bd06eed0c5bb57e1034a4d53"},
 ]
 
 [package.dependencies]
@@ -865,13 +928,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.28.0"
+version = "6.29.4"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.28.0-py3-none-any.whl", hash = "sha256:c6e9a9c63a7f4095c0a22a79f765f079f9ec7be4f2430a898ddea889e8665661"},
-    {file = "ipykernel-6.28.0.tar.gz", hash = "sha256:69c11403d26de69df02225916f916b37ea4b9af417da0a8c827f84328d88e5f3"},
+    {file = "ipykernel-6.29.4-py3-none-any.whl", hash = "sha256:1181e653d95c6808039c509ef8e67c4126b3b3af7781496c7cbfb5ed938a27da"},
+    {file = "ipykernel-6.29.4.tar.gz", hash = "sha256:3d44070060f9475ac2092b760123fadf105d2e2493c24848b6691a7c4f42af5c"},
 ]
 
 [package.dependencies]
@@ -894,7 +957,7 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
@@ -935,21 +998,21 @@ test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "num
 
 [[package]]
 name = "ipywidgets"
-version = "8.1.1"
+version = "8.1.2"
 description = "Jupyter interactive widgets"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"},
-    {file = "ipywidgets-8.1.1.tar.gz", hash = "sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8"},
+    {file = "ipywidgets-8.1.2-py3-none-any.whl", hash = "sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60"},
+    {file = "ipywidgets-8.1.2.tar.gz", hash = "sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9"},
 ]
 
 [package.dependencies]
 comm = ">=0.1.3"
 ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0.9,<3.1.0"
+jupyterlab-widgets = ">=3.0.10,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.9,<4.1.0"
+widgetsnbextension = ">=4.0.10,<4.1.0"
 
 [package.extras]
 test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
@@ -1017,18 +1080,15 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.9.14"
+version = "0.9.24"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
-    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
+    {file = "json5-0.9.24-py3-none-any.whl", hash = "sha256:4ca101fd5c7cb47960c055ef8f4d0e31e15a7c6c48c3b6f1473fc83b6c462a13"},
+    {file = "json5-0.9.24.tar.gz", hash = "sha256:0c638399421da959a20952782800e5c1a78c14e08e1dc9738fa10d8ec14d58c8"},
 ]
 
-[package.extras]
-dev = ["hypothesis"]
-
 [[package]]
 name = "jsonpointer"
 version = "2.4"
@@ -1042,13 +1102,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.20.0"
+version = "4.21.1"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.20.0-py3-none-any.whl", hash = "sha256:ed6231f0429ecf966f5bc8dfef245998220549cbbcf140f913b7464c52c3b6b3"},
-    {file = "jsonschema-4.20.0.tar.gz", hash = "sha256:4f614fd46d8d61258610998997743ec5492a648b33cf478c1ddc23ed4598a5fa"},
+    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
+    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
 ]
 
 [package.dependencies]
@@ -1105,13 +1165,13 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.0"
+version = "8.6.1"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
-    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
+    {file = "jupyter_client-8.6.1-py3-none-any.whl", hash = "sha256:3b7bd22f058434e3b9a7ea4b1500ed47de2713872288c0d511d19926f99b459f"},
+    {file = "jupyter_client-8.6.1.tar.gz", hash = "sha256:e842515e2bab8e19186d89fdfea7abd15e39dd581f94e399f00e2af5a1652d3f"},
 ]
 
 [package.dependencies]
@@ -1151,13 +1211,13 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.1"
+version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
-    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
 ]
 
 [package.dependencies]
@@ -1167,17 +1227,17 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.9.0"
+version = "0.10.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
-    {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"},
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
 ]
 
 [package.dependencies]
@@ -1196,13 +1256,13 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.2"
+version = "2.2.4"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter-lsp-2.2.2.tar.gz", hash = "sha256:256d24620542ae4bba04a50fc1f6ffe208093a07d8e697fea0a8d1b8ca1b7e5b"},
-    {file = "jupyter_lsp-2.2.2-py3-none-any.whl", hash = "sha256:3b95229e4168355a8c91928057c1621ac3510ba98b2a925e82ebd77f078b1aa5"},
+    {file = "jupyter-lsp-2.2.4.tar.gz", hash = "sha256:5e50033149344065348e688608f3c6d654ef06d9856b67655bd7b6bac9ee2d59"},
+    {file = "jupyter_lsp-2.2.4-py3-none-any.whl", hash = "sha256:da61cb63a16b6dff5eac55c2699cc36eac975645adee02c41bdfc03bf4802e77"},
 ]
 
 [package.dependencies]
@@ -1210,13 +1270,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.12.3"
+version = "2.13.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.12.3-py3-none-any.whl", hash = "sha256:6f85310ea5e6068568a521f079fba99d8d17e4884dd1d602ab0f43b3115204a8"},
-    {file = "jupyter_server-2.12.3.tar.gz", hash = "sha256:a1d2d51e497b1a6256c48b6940b0dd49b2553981baf1690077c37792f1fa23a1"},
+    {file = "jupyter_server-2.13.0-py3-none-any.whl", hash = "sha256:77b2b49c3831fbbfbdb5048cef4350d12946191f833a24e5f83e5f8f4803e97b"},
+    {file = "jupyter_server-2.13.0.tar.gz", hash = "sha256:c80bfb049ea20053c3d9641c2add4848b38073bf79f1729cea1faed32fc1c78e"},
 ]
 
 [package.dependencies]
@@ -1242,17 +1302,17 @@ websocket-client = "*"
 
 [package.extras]
 docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
-test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.5.1"
+version = "0.5.3"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server_terminals-0.5.1-py3-none-any.whl", hash = "sha256:5e63e947ddd97bb2832db5ef837a258d9ccd4192cd608c1270850ad947ae5dd7"},
-    {file = "jupyter_server_terminals-0.5.1.tar.gz", hash = "sha256:16d3be9cf48be6a1f943f3a6c93c033be259cf4779184c66421709cf63dccfea"},
+    {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
+    {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
 ]
 
 [package.dependencies]
@@ -1265,17 +1325,18 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.0.11"
+version = "4.1.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.0.11-py3-none-any.whl", hash = "sha256:536bf0e78723153a5016ca7efb88ed0ecd7070d3f1555d5b0e2770658f900a3c"},
-    {file = "jupyterlab-4.0.11.tar.gz", hash = "sha256:d1aec24712566bc25a36229788242778e498ca4088028e2f9aa156b8b7fdc8fc"},
+    {file = "jupyterlab-4.1.5-py3-none-any.whl", hash = "sha256:3bc843382a25e1ab7bc31d9e39295a9f0463626692b7995597709c0ab236ab2c"},
+    {file = "jupyterlab-4.1.5.tar.gz", hash = "sha256:c9ad75290cb10bfaff3624bf3fbb852319b4cce4c456613f8ebbaa98d03524db"},
 ]
 
 [package.dependencies]
 async-lru = ">=1.0.0"
+httpx = ">=0.25.0"
 ipykernel = "*"
 jinja2 = ">=3.0.3"
 jupyter-core = "*"
@@ -1288,9 +1349,9 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.1.6)"]
-docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
-docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.2.0)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.2.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.1)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post6)", "matplotlib (==3.8.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 
 [[package]]
@@ -1306,13 +1367,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.2"
+version = "2.25.4"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.2-py3-none-any.whl", hash = "sha256:5b1798c9cc6a44f65c757de9f97fc06fc3d42535afbf47d2ace5e964ab447aaf"},
-    {file = "jupyterlab_server-2.25.2.tar.gz", hash = "sha256:bd0ec7a99ebcedc8bcff939ef86e52c378e44c2707e053fcd81d046ce979ee63"},
+    {file = "jupyterlab_server-2.25.4-py3-none-any.whl", hash = "sha256:eb645ecc8f9b24bac5decc7803b6d5363250e16ec5af814e516bc2c54dd88081"},
+    {file = "jupyterlab_server-2.25.4.tar.gz", hash = "sha256:2098198e1e82e0db982440f9b5136175d73bea2cd42a6480aa6fd502cb23c4f9"},
 ]
 
 [package.dependencies]
@@ -1327,32 +1388,32 @@ requests = ">=2.31"
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.9"
+version = "3.0.10"
 description = "Jupyter interactive widgets for JupyterLab"
 optional = false
 python-versions = ">=3.7"
 files = [
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+markdown-it-py = ">=1.0"
 mdit-py-plugins = "*"
 nbformat = "*"
 packaging = "*"
@@ -1601,13 +1662,13 @@ rapidfuzz = ">=3.1.0,<4.0.0"
 
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 ]
 
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 [[package]]
@@ -2052,13 +2113,13 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
 name = "nbclient"
-version = "0.9.0"
+version = "0.10.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2070,17 +2131,17 @@ traitlets = ">=5.4"
 [package.extras]
 dev = ["pre-commit"]
 docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
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+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.14.0"
-description = "Converting Jupyter Notebooks"
+version = "7.16.3"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2106,18 +2167,18 @@ docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sp
 qtpdf = ["nbconvert[qtpng]"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
 webpdf = ["playwright"]
 
 [[package]]
 name = "nbformat"
-version = "5.9.2"
+version = "5.10.3"
 description = "The Jupyter Notebook format"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2132,13 +2193,13 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
 name = "nest-asyncio"
-version = "1.5.8"
+version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
 files = [
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 ]
 
 [[package]]
@@ -2161,18 +2222,18 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "notebook"
-version = "7.0.7"
+version = "7.1.2"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 jupyter-server = ">=2.4.0,<3"
-jupyterlab = ">=4.0.2,<5"
+jupyterlab = ">=4.1.1,<4.2"
 jupyterlab-server = ">=2.22.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
@@ -2184,13 +2245,13 @@ test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4
 
 [[package]]
 name = "notebook-shim"
-version = "0.2.3"
+version = "0.2.4"
 description = "A shim layer for notebook traits and config"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2201,47 +2262,47 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
 [[package]]
 name = "numpy"
-version = "1.26.3"
+version = "1.26.4"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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 [[package]]
@@ -2365,14 +2426,14 @@ files = [
 
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+version = "12.4.99"
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 python-versions = ">=3"
 files = [
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 [[package]]
@@ -2402,24 +2463,24 @@ et-xmlfile = "*"
 
 [[package]]
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+version = "7.7.0"
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 python-versions = ">=3.6"
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 [[package]]
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 python-versions = ">=3.7"
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 [[package]]
@@ -2506,13 +2567,13 @@ xml = ["lxml (>=4.9.2)"]
 
 [[package]]
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+version = "1.5.1"
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 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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 [[package]]
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+name = "patsy"
+version = "0.5.6"
+description = "A Python package for describing statistical models and for building design matrices."
+optional = false
+python-versions = "*"
+files = [
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+[package.dependencies]
+numpy = ">=1.4"
+six = "*"
+
+[package.extras]
+test = ["pytest", "pytest-cov", "scipy"]
+
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -2643,18 +2722,18 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.1.0"
+version = "4.2.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
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+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
 
 [[package]]
 name = "pluggy"
@@ -2673,13 +2752,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.19.0"
+version = "0.20.0"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2701,27 +2780,27 @@ wcwidth = "*"
 
 [[package]]
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-version = "5.9.7"
+version = "5.9.8"
 description = "Cross-platform lib for process and system monitoring in Python."
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 ]
 
 [package.extras]
@@ -2754,13 +2833,13 @@ tests = ["pytest"]
 
 [[package]]
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-version = "2.21"
+version = "2.22"
 description = "C parser in Python"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.8"
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 [[package]]
@@ -2780,13 +2859,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.7"
+version = "10.7.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2798,13 +2877,13 @@ extra = ["pygments (>=2.12)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.1"
+version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
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 [package.extras]
@@ -2850,13 +2929,13 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
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-version = "2.8.2"
+version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2875,13 +2954,13 @@ files = [
 
 [[package]]
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-version = "2023.3.post1"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -2909,17 +2988,17 @@ files = [
 
 [[package]]
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-version = "2.0.12"
+version = "2.0.13"
 description = "Pseudo terminal support for Windows from Python."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [[package]]
@@ -3145,101 +3224,101 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
 [[package]]
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-version = "3.6.1"
+version = "3.7.0"
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 python-versions = ">=3.8"
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 [[package]]
 name = "seaborn"
@@ -3698,13 +3777,13 @@ files = [
 
 [[package]]
 name = "sniffio"
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+version = "1.3.1"
 description = "Sniff out which async library your code is running under"
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 python-versions = ">=3.7"
 files = [
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 [[package]]
@@ -3737,6 +3816,56 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
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+[package.extras]
+build = ["cython (>=0.29.33)"]
+develop = ["colorama", "cython (>=0.29.33)", "cython (>=0.29.33,<4.0.0)", "flake8", "isort", "joblib", "matplotlib (>=3)", "oldest-supported-numpy (>=2022.4.18)", "pytest (>=7.3.0)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
+docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
+
 [[package]]
 name = "sympy"
 version = "1.12"
@@ -3767,13 +3896,13 @@ pytest = ">=7.0.0,<9.0.0"
 
 [[package]]
 name = "terminado"
-version = "0.18.0"
+version = "0.18.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "terminado-0.18.0-py3-none-any.whl", hash = "sha256:87b0d96642d0fe5f5abd7783857b9cab167f221a39ff98e3b9619a788a3c0f2e"},
-    {file = "terminado-0.18.0.tar.gz", hash = "sha256:1ea08a89b835dd1b8c0c900d92848147cef2537243361b2e3f4dc15df9b6fded"},
+    {file = "terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0"},
+    {file = "terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"},
 ]
 
 [package.dependencies]
@@ -3788,24 +3917,24 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "threadpoolctl"
-version = "3.2.0"
+version = "3.4.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
-    {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
+    {file = "threadpoolctl-3.4.0-py3-none-any.whl", hash = "sha256:8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262"},
+    {file = "threadpoolctl-3.4.0.tar.gz", hash = "sha256:f11b491a03661d6dd7ef692dd422ab34185d982466c49c8f98c8f716b5c93196"},
 ]
 
 [[package]]
 name = "tifffile"
-version = "2023.12.9"
+version = "2024.2.12"
 description = "Read and write TIFF files"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "tifffile-2023.12.9-py3-none-any.whl", hash = "sha256:9b066e4b1a900891ea42ffd33dab8ba34c537935618b9893ddef42d7d422692f"},
-    {file = "tifffile-2023.12.9.tar.gz", hash = "sha256:9dd1da91180a6453018a241ff219e1905f169384355cd89c9ef4034c1b46cdb8"},
+    {file = "tifffile-2024.2.12-py3-none-any.whl", hash = "sha256:870998f82fbc94ff7c3528884c1b0ae54863504ff51dbebea431ac3fa8fb7c21"},
+    {file = "tifffile-2024.2.12.tar.gz", hash = "sha256:4920a3ec8e8e003e673d3c6531863c99eedd570d1b8b7e141c072ed78ff8030d"},
 ]
 
 [package.dependencies]
@@ -3845,21 +3974,21 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.2.0+cu121"
+version = "2.2.2+cu121"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "torch-2.2.0+cu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:c441021672ebe2e5afbdb34817aa85e6d32130f94df2da9ad4cb78a9d4b81370"},
-    {file = "torch-2.2.0+cu121-cp310-cp310-win_amd64.whl", hash = "sha256:8f54c647ee19c8b4c0aad158c73b83b2c06cb62351e9cfa981540ce7295a9015"},
-    {file = "torch-2.2.0+cu121-cp311-cp311-linux_x86_64.whl", hash = "sha256:0bc59ae71528f0a6013f1b01670f039cc6d01b2ced7a7219ca16ee194c305116"},
-    {file = "torch-2.2.0+cu121-cp311-cp311-win_amd64.whl", hash = "sha256:d79324159c622243429ec214a86b8613c1d7d46fc4821374d324800f1df6ade1"},
-    {file = "torch-2.2.0+cu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:330293cdae296bdb7b925412c561ec3d53cdc82c38104e43385fdbc4eb8f0e72"},
-    {file = "torch-2.2.0+cu121-cp312-cp312-win_amd64.whl", hash = "sha256:26ddc071aec9ac36beaded4036bf0c1ca04a8c82cbdf8615376948761b5f304a"},
-    {file = "torch-2.2.0+cu121-cp38-cp38-linux_x86_64.whl", hash = "sha256:a27daf0405b924359795c39a5a73ff31151880d63f65e3f0051c22f9bb3b231e"},
-    {file = "torch-2.2.0+cu121-cp38-cp38-win_amd64.whl", hash = "sha256:e28673afadfb189bc3ef24683674db908af1f823d8bf60315745d8428668edf5"},
-    {file = "torch-2.2.0+cu121-cp39-cp39-linux_x86_64.whl", hash = "sha256:71c631faed5358961d2fa582a370e1d29f8bec64fc02eb6ff6f4eb2c56acfd85"},
-    {file = "torch-2.2.0+cu121-cp39-cp39-win_amd64.whl", hash = "sha256:ea5b283a8f3ae2b7919ed5d83827664883476560f1161b3e17129827511d8568"},
+    {file = "torch-2.2.2+cu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:cade4fd6c8ce7d826dbcfabd65f1d53b0ee0a058db8c1809d65bfd6051b55530"},
+    {file = "torch-2.2.2+cu121-cp310-cp310-win_amd64.whl", hash = "sha256:d300055aac0e2063f9a2659924e9766605db06d5683532c6eabbdef6bec865dd"},
+    {file = "torch-2.2.2+cu121-cp311-cp311-linux_x86_64.whl", hash = "sha256:4c94e4d1a22d70abbdff716dec99ba5eff94b4340ffa73b4fb629f940dbb8a75"},
+    {file = "torch-2.2.2+cu121-cp311-cp311-win_amd64.whl", hash = "sha256:efbcfdd4399197d06b32f7c0e1711c615188cdd65427b933648c7478fb880b3f"},
+    {file = "torch-2.2.2+cu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:badc14d413ff1847d15021a1ec0affa479d24dfc83e6d51b9b4b9fbfaad1b14c"},
+    {file = "torch-2.2.2+cu121-cp312-cp312-win_amd64.whl", hash = "sha256:5b5b91c7fcda5f02e5e5644a32f593c6c17f301a1180213e353e34b51cc63b9f"},
+    {file = "torch-2.2.2+cu121-cp38-cp38-linux_x86_64.whl", hash = "sha256:c178beb2bb01f773601777bc481c7651be5b1f189cf180f0c0aceac0789aa9a5"},
+    {file = "torch-2.2.2+cu121-cp38-cp38-win_amd64.whl", hash = "sha256:aa67db6ad36d42305eac8236d8412d9fecea81f965cc0b374581cbd2b846ad0d"},
+    {file = "torch-2.2.2+cu121-cp39-cp39-linux_x86_64.whl", hash = "sha256:114e9395867ee860166562d8cc1f2809225f9e29783dd5e72175d9a9a7a8505c"},
+    {file = "torch-2.2.2+cu121-cp39-cp39-win_amd64.whl", hash = "sha256:2de773282a7855dd39139aabc37ffc4ba1b4b28b4594e5f56dd30010b064e8b0"},
 ]
 
 [package.dependencies]
@@ -3879,7 +4008,7 @@ nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"
 nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -3893,28 +4022,27 @@ reference = "torch_cuda121"
 
 [[package]]
 name = "torchvision"
-version = "0.17.0+cu121"
+version = "0.17.2+cu121"
 description = "image and video datasets and models for torch deep learning"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "torchvision-0.17.0+cu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:e1273e9862fc821febc4c716f13849b1ffa87c0d69f6ab824356c5532c490f08"},
-    {file = "torchvision-0.17.0+cu121-cp310-cp310-win_amd64.whl", hash = "sha256:b5ba1adc6f9f1a40af9608ebc447ceed6c8816dcb926d59675c81111b8676966"},
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-    {file = "torchvision-0.17.0+cu121-cp311-cp311-win_amd64.whl", hash = "sha256:307e52c2887c1d2b50cc3581cf5f4c169130b8352462e361e71eeda19e0dd263"},
-    {file = "torchvision-0.17.0+cu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:c39ee5b3e832b88137e10b0f99e9d0b7c13fa73fc00e1469e8b88d8f657eb80e"},
-    {file = "torchvision-0.17.0+cu121-cp312-cp312-win_amd64.whl", hash = "sha256:ad2c7f7c317ed9ebe0ebe9dcf805393320a0fec81c7fdec95f971cdd8884518f"},
-    {file = "torchvision-0.17.0+cu121-cp38-cp38-linux_x86_64.whl", hash = "sha256:de5dbc480fcc100792e3aacf90edffe5bcee4ced3abc4fc1d210d2347fc36e05"},
-    {file = "torchvision-0.17.0+cu121-cp38-cp38-win_amd64.whl", hash = "sha256:1c58b331dc8a46bd378b6776a841d067f739c1699085d1387159e4aed4d0f088"},
-    {file = "torchvision-0.17.0+cu121-cp39-cp39-linux_x86_64.whl", hash = "sha256:bf50506b5870848e5cf0a57e6526b9d00cf4e7c69929491ba91fb9fc384abc38"},
-    {file = "torchvision-0.17.0+cu121-cp39-cp39-win_amd64.whl", hash = "sha256:8036fc7815025dab85bccef3ead1102afc3236e9b16c42c132402cbb06bc6afe"},
+    {file = "torchvision-0.17.2+cu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:c0f325635f8834fa55e69ab61075fb2bbcbb45395a985bba1db378b15627104b"},
+    {file = "torchvision-0.17.2+cu121-cp310-cp310-win_amd64.whl", hash = "sha256:c780783d4113cf3f86297f1b51a0f3a5c1f9487c5ec690b9a470d7d6e4f67e62"},
+    {file = "torchvision-0.17.2+cu121-cp311-cp311-linux_x86_64.whl", hash = "sha256:059f86a1c8d2b276b266c84a8f5a92cc8426d43c2a2c248dc73c140ab3a822f3"},
+    {file = "torchvision-0.17.2+cu121-cp311-cp311-win_amd64.whl", hash = "sha256:10ad542aab6b47dbe73c441381986d50a7ed5021cbe01d593a14477ec1f067a0"},
+    {file = "torchvision-0.17.2+cu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:958d5438a56e83f22dedbb78a3b4301688d5ac433845650d2ccba3285fd27213"},
+    {file = "torchvision-0.17.2+cu121-cp312-cp312-win_amd64.whl", hash = "sha256:ecd9d4eb4f68a144da9936d2760ca3f4cbbfa092fc8d744ca746afff9f4053be"},
+    {file = "torchvision-0.17.2+cu121-cp38-cp38-linux_x86_64.whl", hash = "sha256:df8e74cbbb4de0a60f11cd7c28d820116027cd3949893f97c55b6f12326bd7af"},
+    {file = "torchvision-0.17.2+cu121-cp38-cp38-win_amd64.whl", hash = "sha256:8bbd04880ed39e0f12f78dbb52dcd88b00ffc65fe834059fc0482cb5999f5711"},
+    {file = "torchvision-0.17.2+cu121-cp39-cp39-linux_x86_64.whl", hash = "sha256:f37f592e3128bf3d99968ada5a128f118f9aed1dab609e272ac7231146fc6843"},
+    {file = "torchvision-0.17.2+cu121-cp39-cp39-win_amd64.whl", hash = "sha256:f37a529e82a633db0b7e9a8d37503d14d8f1033c454f80c844c1595637519b98"},
 ]
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
-requests = "*"
-torch = "2.2.0"
+torch = "2.2.2"
 
 [package.extras]
 scipy = ["scipy"]
@@ -3946,18 +4074,18 @@ files = [
 
 [[package]]
 name = "traitlets"
-version = "5.14.1"
+version = "5.14.2"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
-    {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
+    {file = "traitlets-5.14.2-py3-none-any.whl", hash = "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"},
+    {file = "traitlets-5.14.2.tar.gz", hash = "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.1)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "triton"
@@ -3984,35 +4112,35 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.8.19.20240106"
+version = "2.9.0.20240316"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"},
-    {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"},
+    {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
+    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.10.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
 
 [[package]]
 name = "tzdata"
-version = "2023.4"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
-    {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [[package]]
@@ -4031,54 +4159,57 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.1.0"
+version = "2.2.1"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
-    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
 ]
 
 [package.extras]
 brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "watchdog"
-version = "3.0.0"
+version = "4.0.0"
 description = "Filesystem events monitoring"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:336adfc6f5cc4e037d52db31194f7581ff744b67382eb6021c868322e32eef41"},
-    {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a70a8dcde91be523c35b2bf96196edc5730edb347e374c7de7cd20c43ed95397"},
-    {file = "watchdog-3.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:adfdeab2da79ea2f76f87eb42a3ab1966a5313e5a69a0213a3cc06ef692b0e96"},
-    {file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b57a1e730af3156d13b7fdddfc23dea6487fceca29fc75c5a868beed29177ae"},
-    {file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7ade88d0d778b1b222adebcc0927428f883db07017618a5e684fd03b83342bd9"},
-    {file = "watchdog-3.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7e447d172af52ad204d19982739aa2346245cc5ba6f579d16dac4bfec226d2e7"},
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+    {file = "watchdog-4.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:6a80d5cae8c265842c7419c560b9961561556c4361b297b4c431903f8c33b269"},
+    {file = "watchdog-4.0.0-py3-none-win32.whl", hash = "sha256:8f9a542c979df62098ae9c58b19e03ad3df1c9d8c6895d96c0d51da17b243b1c"},
+    {file = "watchdog-4.0.0-py3-none-win_amd64.whl", hash = "sha256:f970663fa4f7e80401a7b0cbeec00fa801bf0287d93d48368fc3e6fa32716245"},
+    {file = "watchdog-4.0.0-py3-none-win_ia64.whl", hash = "sha256:9a03e16e55465177d416699331b0f3564138f1807ecc5f2de9d55d8f188d08c7"},
+    {file = "watchdog-4.0.0.tar.gz", hash = "sha256:e3e7065cbdabe6183ab82199d7a4f6b3ba0a438c5a512a68559846ccb76a78ec"},
 ]
 
 [package.extras]
@@ -4139,13 +4270,13 @@ test = ["websockets"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.9"
+version = "4.0.10"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"},
-    {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
+    {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
+    {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
 ]
 
 [[package]]
@@ -4268,4 +4399,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11,<3.13"
-content-hash = "4d8b63883afaa59e69b29f664f74a14bdcac7edb56e83975a45abf92586a3a73"
+content-hash = "234bdb751d737d5fe454908cc083833ae668716e9a990813c1c23265954a55b2"
diff --git a/pyproject.toml b/pyproject.toml
index 58b135b8a..4082ab08f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,6 +26,7 @@ seaborn = "^0.13.0"
 torch = {version = "^2.2.0", source = "torch_cuda121"}
 torchvision = {version = "^0.17.0", source = "torch_cuda121"}
 xxhash = "^3.4.1"
+statsmodels = "^0.14.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.2.1,<9.0.0"
diff --git a/src/safeds/data/tabular/containers/_time_series.py b/src/safeds/data/tabular/containers/_time_series.py
index 105c29258..a26836b61 100644
--- a/src/safeds/data/tabular/containers/_time_series.py
+++ b/src/safeds/data/tabular/containers/_time_series.py
@@ -21,6 +21,7 @@
 
 if TYPE_CHECKING:
     from collections.abc import Callable, Mapping, Sequence
+    from pathlib import Path
     from typing import Any
 
 
@@ -29,6 +30,57 @@ class TimeSeries(Table):
     # ------------------------------------------------------------------------------------------------------------------
     # Creation
     # ------------------------------------------------------------------------------------------------------------------
+
+    @staticmethod
+    def timeseries_from_csv_file(
+        path: str | Path,
+        target_name: str,
+        time_name: str,
+        feature_names: list[str] | None = None,
+    ) -> TimeSeries:
+        """
+        Read data from a CSV file into a table.
+
+        Parameters
+        ----------
+        path:
+            The path to the CSV file.
+
+        target_name:
+            The name of the target column
+
+        time_name:
+            The name of the time column
+
+        feature_names:
+            The name(s) of the column(s)
+
+        Returns
+        -------
+        table:
+            The time series created from the CSV file.
+
+        Raises
+        ------
+        FileNotFoundError
+            If the specified file does not exist.
+        WrongFileExtensionError
+            If the file is not a csv file.
+        UnknownColumnNameError
+            If target_name or time_name matches none of the column names.
+        Value Error
+            If one column is target and feature
+        Value Error
+            If one column is time and feature
+
+        """
+        return TimeSeries._from_table(
+            Table.from_csv_file(path=path),
+            target_name=target_name,
+            time_name=time_name,
+            feature_names=feature_names,
+        )
+
     @staticmethod
     def _from_tagged_table(
         tagged_table: TaggedTable,
@@ -128,12 +180,17 @@ def _from_table(
 
         if target_name not in table.column_names:
             raise UnknownColumnNameError([target_name])
-
         result = object.__new__(TimeSeries)
         result._data = table._data
+
         result._schema = table._schema
         result._time = table.get_column(time_name)
         result._target = table.get_column(target_name)
+        # empty Columns have dtype Object
+        if len(result._time._data) == 0:
+            result._time._data = pd.Series(name=time_name)
+        if len(result.target._data) == 0:
+            result.target._data = pd.Series(name=target_name)
         if feature_names is None or len(feature_names) == 0:
             result._feature_names = []
             result._features = Table()
@@ -203,6 +260,11 @@ def __init__(
             raise UnknownColumnNameError([time_name])
         self._time: Column = _data.get_column(time_name)
         self._target: Column = _data.get_column(target_name)
+        # empty Columns have dtype Object
+        if len(self._time._data) == 0:
+            self._time._data = pd.Series(name=time_name)
+        if len(self.target._data) == 0:
+            self.target._data = pd.Series(name=target_name)
 
     def __eq__(self, other: object) -> bool:
         """
@@ -216,6 +278,7 @@ def __eq__(self, other: object) -> bool:
             return NotImplemented
         if self is other:
             return True
+
         return (
             self.time == other.time
             and self.target == other.target
@@ -1113,3 +1176,102 @@ def plot_scatterplot(
         buffer.seek(0)
         self._data = self._data.reset_index()
         return Image.from_bytes(buffer.read())
+
+    def split_rows(self, percentage_in_first: float) -> tuple[TimeSeries, TimeSeries]:
+        """
+        Split the table into two new tables.
+
+        The original time series is not modified.
+
+        Parameters
+        ----------
+        percentage_in_first:
+            The desired size of the first time series in percentage to the given time series; must be between 0 and 1.
+
+        Returns
+        -------
+        result:
+            A tuple containing the two resulting time series. The first time series has the specified size, the second time series
+            contains the rest of the data.
+
+        Raises
+        ------
+        ValueError:
+            if the 'percentage_in_first' is not between 0 and 1.
+
+        Examples
+        --------
+        >>> from safeds.data.tabular.containers import TimeSeries
+        >>> time_series = TimeSeries({"time":[0, 1, 2, 3, 4], "temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]}, time_name="time", target_name="sales")
+        >>> slices = time_series.split_rows(0.4)
+        >>> slices[0]
+           time  temperature  sales
+        0     0           10     54
+        1     1           15     74
+        >>> slices[1]
+           time  temperature  sales
+        0     2           20     90
+        1     3           25    206
+        2     4           30    210
+        """
+        temp = self._as_table()
+        t1, t2 = temp.split_rows(percentage_in_first=percentage_in_first)
+        return (
+            TimeSeries._from_table(
+                t1,
+                time_name=self.time.name,
+                target_name=self._target.name,
+                feature_names=self._feature_names,
+            ),
+            TimeSeries._from_table(
+                t2,
+                time_name=self.time.name,
+                target_name=self._target.name,
+                feature_names=self._feature_names,
+            ),
+        )
+
+    def plot_compare_time_series(self, time_series: list[TimeSeries]) -> Image:
+        """
+        Plot the given time series targets along the time on the x-axis.
+
+        Parameters
+        ----------
+        time_series:
+            A list of time series to be plotted.
+
+        Returns
+        -------
+        plot:
+              A plot with all the time series targets plotted by the time on the x-axis.
+
+        Raises
+        ------
+        NonNumericColumnError
+            if the target column contains non numerical values
+
+        """
+        if not self._target.type.is_numeric():
+            raise NonNumericColumnError("The time series plotted column contains non-numerical columns.")
+
+        data = pd.DataFrame()
+        data[self.time.name] = self.time._data
+        data[self.target.name] = self.target._data
+        for index, ts in enumerate(time_series):
+            if not ts.target.type.is_numeric():
+                raise NonNumericColumnError("The time series plotted column contains non-numerical columns.")
+            data[ts.target.name + " " + str(index)] = ts.target._data
+        fig = plt.figure()
+
+        data = pd.melt(data, [self.time.name])
+        sns.lineplot(x=self.time.name, y="value", hue="variable", data=data)
+        plt.title("Multiple Series Plot")
+        plt.xlabel("Time")
+
+        plt.tight_layout()
+        buffer = io.BytesIO()
+        fig.savefig(buffer, format="png")
+        plt.close()  # Prevents the figure from being displayed directly
+        buffer.seek(0)
+        self._data = self._data.reset_index()
+        return Image.from_bytes(buffer.read())
diff --git a/src/safeds/exceptions/__init__.py b/src/safeds/exceptions/__init__.py
index ca193270b..4c1c60331 100644
--- a/src/safeds/exceptions/__init__.py
+++ b/src/safeds/exceptions/__init__.py
@@ -28,6 +28,7 @@
     DatasetMissesFeaturesError,
     LearningError,
     ModelNotFittedError,
+    NonTimeSeriesError,
     PredictionError,
     UntaggedTableError,
 )
@@ -56,6 +57,7 @@
     "DatasetMissesFeaturesError",
     "LearningError",
     "ModelNotFittedError",
+    "NonTimeSeriesError",
     "PredictionError",
     "UntaggedTableError",
     # Other
diff --git a/src/safeds/exceptions/_ml.py b/src/safeds/exceptions/_ml.py
index 1cc83ebc0..933d52a75 100644
--- a/src/safeds/exceptions/_ml.py
+++ b/src/safeds/exceptions/_ml.py
@@ -78,3 +78,15 @@ def __init__(self) -> None:
                 " features and which are the target to predict.\nUse Table.tag_column() to create a tagged table."
             ),
         )
+
+
+class NonTimeSeriesError(Exception):
+    """Raised when a table is used instead of a TimeSeries in a regression or classification."""
+
+    def __init__(self) -> None:
+        super().__init__(
+            (
+                "This method needs a time series.\nA time series is a table that additionally knows which columns are"
+                " time and which are the target to predict.\n"
+            ),
+        )
diff --git a/src/safeds/ml/classical/regression/__init__.py b/src/safeds/ml/classical/regression/__init__.py
index 7c042454f..1bd71ac05 100644
--- a/src/safeds/ml/classical/regression/__init__.py
+++ b/src/safeds/ml/classical/regression/__init__.py
@@ -1,6 +1,7 @@
 """Models for regression tasks."""
 
 from ._ada_boost import AdaBoostRegressor
+from ._arima import ArimaModelRegressor
 from ._decision_tree import DecisionTreeRegressor
 from ._elastic_net_regression import ElasticNetRegressor
 from ._gradient_boosting import GradientBoostingRegressor
@@ -14,6 +15,7 @@
 
 __all__ = [
     "AdaBoostRegressor",
+    "ArimaModelRegressor",
     "DecisionTreeRegressor",
     "ElasticNetRegressor",
     "GradientBoostingRegressor",
diff --git a/src/safeds/ml/classical/regression/_arima.py b/src/safeds/ml/classical/regression/_arima.py
new file mode 100644
index 000000000..a9a2705da
--- /dev/null
+++ b/src/safeds/ml/classical/regression/_arima.py
@@ -0,0 +1,209 @@
+from __future__ import annotations
+
+import io
+import itertools
+
+import matplotlib.pyplot as plt
+import xxhash
+from statsmodels.tsa.arima.model import ARIMA
+
+from safeds.data.image.containers import Image
+from safeds.data.tabular.containers import Column, Table, TimeSeries
+from safeds.exceptions import (
+    DatasetMissesDataError,
+    MissingValuesColumnError,
+    ModelNotFittedError,
+    NonNumericColumnError,
+    NonTimeSeriesError,
+)
+
+
+class ArimaModelRegressor:
+    """Auto Regressive Integrated Moving Average Model."""
+
+    def __hash__(self) -> int:
+        """
+        Return a deterministic hash value for a regressor.
+
+        Returns
+        -------
+        hash:
+            The hash value.
+        """
+        return xxhash.xxh3_64(
+            self.__class__.__qualname__.encode("utf-8")
+            + (1 if self.is_fitted() else 0).to_bytes(1)
+            + (bytes((9, 9, 9)) if self._order is None else bytes(self._order)),
+        ).intdigest()
+
+    def __init__(self) -> None:
+        # Internal state
+        self._arima: ARIMA | None = None
+        self._order: tuple[int, int, int] | None = None
+        self._fitted = False
+
+    def fit(self, time_series: TimeSeries) -> ArimaModelRegressor:
+        """
+        Create a copy of this ARIMA Model and fit it with the given training data.
+
+        This ARIMA Model is not modified.
+
+        Parameters
+        ----------
+        time_series:
+            The time series containing the target column, which will be used.
+
+        Returns
+        -------
+        fitted_arima:
+            The fitted ARIMA Model.
+
+        Raises
+        ------
+        LearningError
+            If the training data contains invalid values or if the training failed.
+        NonTimeSeriesError
+            If the table is not a TimeSeries object.
+        NonNumericColumnError
+            If the training data contains non-numerical values.
+        MissingValuesColumnError
+            If the training data contains missing values.
+        DatasetMissesDataError
+            If the training data contains no rows.
+        """
+        if not isinstance(time_series, TimeSeries) and isinstance(time_series, Table):
+            raise NonTimeSeriesError
+        if time_series.number_of_rows == 0:
+            raise DatasetMissesDataError
+        if not time_series.target.type.is_numeric():
+            raise NonNumericColumnError(time_series.target.name)
+        if time_series.target.has_missing_values():
+            raise MissingValuesColumnError(
+                time_series.target.name,
+                "You can use the Imputer to replace the missing values based on different strategies.\nIf you want to"
+                "remove the missing values entirely you can use the method "
+                "`TimeSeries.remove_rows_with_missing_values`.",
+            )
+        fitted_arima = ArimaModelRegressor()
+        p = d = q = range(2)
+        pdq = list(itertools.product(p, d, q))
+        best_aic = float("inf")
+        best_model = None
+        # best param will get overwritten
+        best_param = (0, 0, 0)
+        for param in pdq:
+            # Create and fit an ARIMA model with the current parameters
+            mod = ARIMA(time_series.target._data.values, order=param)
+
+            # I wasnt able to invoke an learning Error
+            # Add try catch when an learning error is found
+            result = mod.fit()
+
+            # Compare the current model's AIC with the best AIC so far
+            if result.aic < best_aic:
+                best_aic = result.aic
+                best_param = param
+                best_model = result
+
+        fitted_arima._order = best_param
+        fitted_arima._arima = best_model
+        fitted_arima._fitted = True
+        return fitted_arima
+
+    def predict(self, time_series: TimeSeries) -> TimeSeries:
+        """
+        Predict a target vector using a time series target column. The model has to be trained first.
+
+        Parameters
+        ----------
+        time_series:
+            The test dataset of the time series.
+
+        Returns
+        -------
+        time_series:
+            A timeseries containing the predicted target vector and a time dummy as time column.
+
+        Raises
+        ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        IndexError
+            If the forecast horizon is not greater than zero.
+        PredictionError
+            If predicting with the given dataset failed.
+        """
+        # make a table without
+        forecast_horizon = len(time_series.target._data)
+        result_table = time_series._as_table()
+        result_table = result_table.remove_columns([time_series.target.name])
+        # Validation
+        if not self.is_fitted() or self._arima is None:
+            raise ModelNotFittedError
+
+        # forecast
+        # couldn't invoke prediction error, will be added when found
+        forecast_results = self._arima.forecast(steps=forecast_horizon)
+        target_column: Column = Column(name=time_series.target.name + " " + "forecasted", data=forecast_results)
+
+        # create new TimeSeries
+        result_table = result_table.add_column(target_column)
+        return TimeSeries._from_table(
+            result_table,
+            time_name=time_series.time.name,
+            target_name=time_series.target.name + " " + "forecasted",
+            feature_names=time_series.features.column_names,
+        )
+
+    def plot_predictions(self, test_series: TimeSeries) -> Image:
+        """
+        Plot the predictions of the trained model to the given target of the time series. So you can see the predictions and the actual values in one plot.
+
+        Parameters
+        ----------
+        test_series:
+            The time series containing the target vector.
+
+        Returns
+        -------
+        image:
+            Plots predictions of the given time series to the given target Column
+
+        Raises
+        ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        PredictionError
+            If predicting with the given dataset failed.
+
+        """
+        if not self.is_fitted() or self._arima is None:
+            raise ModelNotFittedError
+        test_data = test_series.target._data.to_numpy()
+        n_steps = len(test_data)
+        forecast_results = self._arima.forecast(steps=n_steps)
+
+        fig = plt.figure()
+        plt.plot(
+            forecast_results,
+        )
+        plt.legend(["forecasted"])
+        plt.plot(test_data)
+        plt.tight_layout()
+
+        buffer = io.BytesIO()
+        fig.savefig(buffer, format="png")
+        plt.close()  # Prevents the figure from being displayed directly
+        buffer.seek(0)
+        return Image.from_bytes(buffer.read())
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted:
+            Whether the regressor is fitted.
+        """
+        return self._fitted
diff --git a/tests/helpers/_assertions.py b/tests/helpers/_assertions.py
index e71d1cdaf..a199fae74 100644
--- a/tests/helpers/_assertions.py
+++ b/tests/helpers/_assertions.py
@@ -54,7 +54,7 @@ def assert_that_time_series_are_equal(table1: TimeSeries, table2: TimeSeries) ->
     """
     assert table1.schema == table2.schema
     assert table1._feature_names == table2._feature_names
-    assert table1.features == table2._features
-    assert table1.target == table2._target
+    assert table1.features == table2.features
+    assert table1.target == table2.target
     assert table1.time == table2.time
     assert table1 == table2
diff --git a/tests/resources/_datas/US_Inflation_rates.csv b/tests/resources/_datas/US_Inflation_rates.csv
new file mode 100644
index 000000000..5176069e0
--- /dev/null
+++ b/tests/resources/_datas/US_Inflation_rates.csv
@@ -0,0 +1,919 @@
+date,value
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diff --git a/tests/safeds/data/tabular/containers/_table/test_split_rows.py b/tests/safeds/data/tabular/containers/_table/test_split_rows.py
index e366a65ba..daa5708a6 100644
--- a/tests/safeds/data/tabular/containers/_table/test_split_rows.py
+++ b/tests/safeds/data/tabular/containers/_table/test_split_rows.py
@@ -50,7 +50,6 @@ def test_should_split_table(
 )
 def test_should_raise_if_value_not_in_range(percentage_in_first: float) -> None:
     table = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]})
-
     with pytest.raises(ValueError, match=r"The given percentage is not between 0 and 1"):
         table.split_rows(percentage_in_first)
 
diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_compare_time_series/test_legit_compare.png b/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_compare_time_series/test_legit_compare.png
new file mode 100644
index 000000000..5078adf5c
Binary files /dev/null and b/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_compare_time_series/test_legit_compare.png differ
diff --git a/tests/safeds/data/tabular/containers/_time_series/test_plot_compare_time_series.py b/tests/safeds/data/tabular/containers/_time_series/test_plot_compare_time_series.py
new file mode 100644
index 000000000..47e28ca73
--- /dev/null
+++ b/tests/safeds/data/tabular/containers/_time_series/test_plot_compare_time_series.py
@@ -0,0 +1,113 @@
+import pytest
+from safeds.data.tabular.containers import TimeSeries
+from safeds.exceptions import NonNumericColumnError
+from syrupy import SnapshotAssertion
+
+
+def create_time_series_list() -> list[TimeSeries]:
+    table1 = TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "target": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
+        },
+        target_name="target",
+        time_name="time",
+        feature_names=None,
+    )
+    table2 = TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "feature_1": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
+            "target": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
+        },
+        target_name="target",
+        time_name="time",
+        feature_names=None,
+    )
+    return [table1, table2]
+
+
+def create_invalid_time_series_list() -> list[TimeSeries]:
+    table1 = TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "target": ["9", 10, 11, 12, 13, 14, 15, 16, 17, 18],
+        },
+        target_name="target",
+        time_name="time",
+        feature_names=None,
+    )
+    table2 = TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "feature_1": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
+            "target": ["4", 5, 6, 7, 8, 9, 10, 11, 12, 13],
+        },
+        target_name="target",
+        time_name="time",
+        feature_names=None,
+    )
+    return [table1, table2]
+
+
+def test_legit_compare(snapshot_png: SnapshotAssertion) -> None:
+    table = TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        },
+        target_name="target",
+        time_name="time",
+        feature_names=None,
+    )
+    plot = table.plot_compare_time_series(create_time_series_list())
+    assert plot == snapshot_png
+
+
+def test_should_raise_if_column_contains_non_numerical_values_x() -> None:
+    table = TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+        },
+        target_name="target",
+        time_name="time",
+        feature_names=None,
+    )
+    with pytest.raises(
+        NonNumericColumnError,
+        match=(
+            r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted"
+            r" column"
+            r" contains"
+            r" non-numerical columns."
+        ),
+    ):
+        table.plot_compare_time_series(create_time_series_list())
+
+
+def test_with_non_valid_list() -> None:
+    table = TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        },
+        target_name="target",
+        time_name="time",
+        feature_names=None,
+    )
+    with pytest.raises(
+        NonNumericColumnError,
+        match=(
+            r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted"
+            r" column"
+            r" contains"
+            r" non-numerical columns."
+        ),
+    ):
+        table.plot_compare_time_series(create_invalid_time_series_list())
diff --git a/tests/safeds/data/tabular/containers/_time_series/test_split_rows.py b/tests/safeds/data/tabular/containers/_time_series/test_split_rows.py
new file mode 100644
index 000000000..faee9fc23
--- /dev/null
+++ b/tests/safeds/data/tabular/containers/_time_series/test_split_rows.py
@@ -0,0 +1,69 @@
+import pandas as pd
+import pytest
+from safeds.data.tabular.containers import Table, TimeSeries
+from safeds.data.tabular.typing import Integer, Nothing, Schema
+
+
+@pytest.mark.parametrize(
+    ("table", "result_train_table", "result_test_table", "percentage_in_first"),
+    [
+        (
+            TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"),
+            TimeSeries({"col1": [1, 2], "col2": [1, 2]}, time_name="col1", target_name="col2"),
+            TimeSeries({"col1": [1], "col2": [4]}, time_name="col1", target_name="col2"),
+            2 / 3,
+        ),
+        (
+            TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"),
+            TimeSeries._from_table(
+                Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Nothing(), "col2": Nothing()})),
+                time_name="col1",
+                target_name="col2",
+            ),
+            TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"),
+            0,
+        ),
+        (
+            TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"),
+            TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"),
+            TimeSeries._from_table(
+                Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Integer(), "col2": Integer()})),
+                time_name="col1",
+                target_name="col2",
+            ),
+            1,
+        ),
+    ],
+    ids=["2/3%", "0%", "100%"],
+)
+def test_should_split_table(
+    table: TimeSeries,
+    result_train_table: TimeSeries,
+    result_test_table: TimeSeries,
+    percentage_in_first: int,
+) -> None:
+    train_table, test_table = table.split_rows(percentage_in_first)
+    assert result_test_table == test_table
+    assert result_train_table.schema == train_table.schema
+    assert result_train_table == train_table
+
+
+@pytest.mark.parametrize(
+    "percentage_in_first",
+    [
+        -1.0,
+        2.0,
+    ],
+    ids=["-100%", "200%"],
+)
+def test_should_raise_if_value_not_in_range(percentage_in_first: float) -> None:
+    table = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]})
+
+    with pytest.raises(ValueError, match=r"The given percentage is not between 0 and 1"):
+        table.split_rows(percentage_in_first)
+
+
+def test_should_split_empty_table() -> None:
+    t1, t2 = Table().split_rows(0.4)
+    assert t1.number_of_rows == 0
+    assert t2.number_of_rows == 0
diff --git a/tests/safeds/data/tabular/containers/_time_series/test_timeseries_from_csv_file.py b/tests/safeds/data/tabular/containers/_time_series/test_timeseries_from_csv_file.py
new file mode 100644
index 000000000..0ca5073c6
--- /dev/null
+++ b/tests/safeds/data/tabular/containers/_time_series/test_timeseries_from_csv_file.py
@@ -0,0 +1,62 @@
+from pathlib import Path
+
+import pytest
+from safeds.data.tabular.containers import TimeSeries
+from safeds.exceptions import WrongFileExtensionError
+
+from tests.helpers import resolve_resource_path
+
+
+@pytest.mark.parametrize(
+    ("path", "expected"),
+    [
+        (
+            "table.csv",
+            TimeSeries({"A": [1], "B": [2]}, time_name="A", target_name="B"),
+        ),
+        (Path("table.csv"), TimeSeries({"A": [1], "B": [2]}, time_name="A", target_name="B")),
+    ],
+    ids=["by String", "by path"],
+)
+def test_should_create_table_from_csv_file(path: str | Path, expected: TimeSeries) -> None:
+    table = TimeSeries.timeseries_from_csv_file(resolve_resource_path(path), time_name="A", target_name="B")
+    assert table.schema == expected.schema
+    assert table == expected
+
+
+@pytest.mark.parametrize(
+    ("path", "expected_error_message"),
+    [
+        ("test_table_from_csv_file_invalid.csv", r"test_table_from_csv_file_invalid.csv\" does not exist"),
+        (Path("test_table_from_csv_file_invalid.csv"), r"test_table_from_csv_file_invalid.csv\" does not exist"),
+    ],
+    ids=["by String", "by path"],
+)
+def test_should_raise_error_if_file_not_found(path: str | Path, expected_error_message: str) -> None:
+    with pytest.raises(FileNotFoundError, match=expected_error_message):
+        TimeSeries.timeseries_from_csv_file(resolve_resource_path(path), time_name="A", target_name="B")
+
+
+@pytest.mark.parametrize(
+    ("path", "expected_error_message"),
+    [
+        (
+            "invalid_file_extension.file_extension",
+            (
+                r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the"
+                r" following extension\(s\): .csv"
+            ),
+        ),
+        (
+            Path("invalid_file_extension.file_extension"),
+            (
+                r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the"
+                r" following extension\(s\): .csv"
+            ),
+        ),
+    ],
+    ids=["by String", "by path"],
+)
+def test_should_raise_error_if_wrong_file_extension(path: str | Path, expected_error_message: str) -> None:
+    with pytest.raises(WrongFileExtensionError, match=expected_error_message):
+        TimeSeries.timeseries_from_csv_file(resolve_resource_path(path), time_name="A", target_name="B")
diff --git a/tests/safeds/ml/classical/regression/__snapshots__/test_arimaModel/test_arima_model.png b/tests/safeds/ml/classical/regression/__snapshots__/test_arimaModel/test_arima_model.png
new file mode 100644
index 000000000..027942221
Binary files /dev/null and b/tests/safeds/ml/classical/regression/__snapshots__/test_arimaModel/test_arima_model.png differ
diff --git a/tests/safeds/ml/classical/regression/test_arima_model.py b/tests/safeds/ml/classical/regression/test_arima_model.py
new file mode 100644
index 000000000..b8ca54693
--- /dev/null
+++ b/tests/safeds/ml/classical/regression/test_arima_model.py
@@ -0,0 +1,224 @@
+from typing import Any
+
+import pytest
+from safeds.data.tabular.containers import Table, TimeSeries
+from safeds.exceptions import (
+    DatasetMissesDataError,
+    MissingValuesColumnError,
+    ModelNotFittedError,
+    NonNumericColumnError,
+    NonTimeSeriesError,
+)
+from safeds.ml.classical.regression import ArimaModelRegressor, LassoRegressor
+
+from tests.helpers import resolve_resource_path
+
+
+def test_arima_model() -> None:
+    # Create a DataFrame
+    _inflation_path = "_datas/US_Inflation_rates.csv"
+    time_series = TimeSeries.timeseries_from_csv_file(
+        path=resolve_resource_path(_inflation_path),
+        target_name="value",
+        time_name="date",
+    )
+    train_ts, test_ts = time_series.split_rows(0.8)
+    model = ArimaModelRegressor()
+    trained_model = model.fit(train_ts)
+    predicted_ts = trained_model.predict(test_ts)
+    predicted_ts.plot_compare_time_series([test_ts])
+    # suggest it ran through
+    assert True
+
+
+def create_test_data() -> TimeSeries:
+    return TimeSeries(
+        {"time": [1, 2, 3, 4, 5, 6, 7, 8, 9], "value": [1, 2, 3, 4, 5, 6, 7, 8, 9]},
+        time_name="time",
+        target_name="value",
+    )
+
+
+def create_test_data_with_feature() -> TimeSeries:
+    return TimeSeries(
+        {
+            "time": [1, 2, 3, 4, 5, 6, 7, 8, 9],
+            "value": [1, 2, 3, 4, 5, 6, 7, 8, 9],
+            "feature": [1, 2, 3, 4, 5, 6, 7, 8, 9],
+        },
+        time_name="time",
+        target_name="value",
+    )
+
+
+def test_should_succeed_on_valid_data() -> None:
+    valid_data = create_test_data()
+    model = ArimaModelRegressor()
+    model.fit(valid_data)
+    assert True
+
+
+def test_should_not_change_input_regressor() -> None:
+    valid_data = create_test_data()
+    model = ArimaModelRegressor()
+    model.fit(valid_data)
+    assert not model.is_fitted()
+
+
+def test_should_not_change_input_table() -> None:
+    valid_data = create_test_data()
+    valid_data_copy = create_test_data()
+    model = ArimaModelRegressor()
+    model.fit(valid_data)
+    assert valid_data_copy == valid_data
+
+
+def test_should_succeed_on_valid_data_plot() -> None:
+    valid_data = create_test_data()
+    model = ArimaModelRegressor()
+    fitted_model = model.fit(valid_data)
+    fitted_model.plot_predictions(valid_data)
+    assert True
+
+
+@pytest.mark.parametrize(
+    ("invalid_data", "expected_error", "expected_error_msg"),
+    [
+        (
+            Table(
+                {
+                    "id": [1, 4],
+                    "feat1": [1, 5],
+                    "feat2": [3, 6],
+                    "target": ["0", 1],
+                },
+            ).time_columns(target_name="target", feature_names=["feat1", "feat2"], time_name="id"),
+            NonNumericColumnError,
+            r"Tried to do a numerical operation on one or multiple non-numerical columns: \ntarget",
+        ),
+        (
+            Table(
+                {
+                    "id": [1, 4],
+                    "feat1": [1, 5],
+                    "feat2": [3, 6],
+                    "target": [None, 1],
+                },
+            ).time_columns(target_name="target", feature_names=["feat1", "feat2"], time_name="id"),
+            MissingValuesColumnError,
+            r"Tried to do an operation on one or multiple columns containing missing values: \ntarget\nYou can use the Imputer to replace the missing values based on different strategies.\nIf you want toremove the missing values entirely you can use the method `TimeSeries.remove_rows_with_missing_values`.",
+        ),
+        (
+            Table(
+                {
+                    "id": [],
+                    "feat1": [],
+                    "feat2": [],
+                    "target": [],
+                },
+            ).time_columns(target_name="target", feature_names=["feat1", "feat2"], time_name="id"),
+            DatasetMissesDataError,
+            r"Dataset contains no rows",
+        ),
+    ],
+    ids=["non-numerical data", "missing values in data", "no rows in data"],
+)
+def test_should_raise_on_invalid_data(
+    invalid_data: TimeSeries,
+    expected_error: Any,
+    expected_error_msg: str,
+) -> None:
+    model = ArimaModelRegressor()
+    with pytest.raises(expected_error, match=expected_error_msg):
+        model.fit(invalid_data)
+
+
+@pytest.mark.parametrize(
+    "table",
+    [
+        Table(
+            {
+                "a": [1.0, 0.0, 0.0, 0.0],
+                "b": [0.0, 1.0, 1.0, 0.0],
+                "c": [0.0, 0.0, 0.0, 1.0],
+            },
+        ),
+    ],
+    ids=["untagged_table"],
+)
+def test_should_raise_if_table_is_not_tagged(table: Table) -> None:
+    model = ArimaModelRegressor()
+    with pytest.raises(NonTimeSeriesError):
+        model.fit(table)  # type: ignore[arg-type]
+
+
+def test_correct_structure_of_time_series_with_features() -> None:
+    data = create_test_data_with_feature()
+    model = ArimaModelRegressor()
+    model = model.fit(data)
+    predics_ts = model.predict(data)
+    assert len(predics_ts.time) == len(data.time)
+    assert len(predics_ts.target) == len(data.target)
+    assert predics_ts.time.name == data.time.name
+    assert predics_ts.target.name == data.target.name + " " + "forecasted"
+    assert predics_ts.features.column_names == data.features.column_names
+
+
+def test_correct_structure_of_time_series() -> None:
+    data = create_test_data()
+    model = ArimaModelRegressor()
+    model = model.fit(data)
+    predics_ts = model.predict(data)
+    assert len(predics_ts.time) == len(data.time)
+    assert len(predics_ts.target) == len(data.target)
+    assert predics_ts.time.name == data.time.name
+    assert predics_ts.target.name == data.target.name + " " + "forecasted"
+    assert predics_ts.features.column_names == data.features.column_names
+
+
+def test_should_raise_if_not_fitted() -> None:
+    model = ArimaModelRegressor()
+    with pytest.raises(ModelNotFittedError):
+        model.predict(create_test_data())
+
+
+def test_if_fitted_not_fitted() -> None:
+    model = ArimaModelRegressor()
+    assert not model.is_fitted()
+
+
+def test_if_fitted_fitted() -> None:
+    model = ArimaModelRegressor()
+    model = model.fit(create_test_data())
+    assert model.is_fitted()
+
+
+def test_should_raise_if_horizon_too_small_plot() -> None:
+    model = ArimaModelRegressor()
+    with pytest.raises(ModelNotFittedError):
+        model.plot_predictions(create_test_data())
+
+
+def test_should_return_same_hash_for_equal_regressor() -> None:
+    regressor1 = ArimaModelRegressor()
+    regressor2 = ArimaModelRegressor()
+    assert hash(regressor1) == hash(regressor2)
+
+
+def test_should_return_different_hash_for_unequal_regressor() -> None:
+    regressor1 = ArimaModelRegressor()
+    regressor2 = LassoRegressor()
+    assert hash(regressor1) != hash(regressor2)
+
+
+def test_should_return_different_hash_for_same_regressor_fit() -> None:
+    regressor1 = ArimaModelRegressor()
+    regressor1_fit = regressor1.fit(create_test_data())
+    assert hash(regressor1) != hash(regressor1_fit)
+
+
+def test_should_return_different_hash_for_regressor_fit() -> None:
+    regressor1 = ArimaModelRegressor()
+    regressor2 = ArimaModelRegressor()
+    regressor1_fit = regressor1.fit(create_test_data())
+    assert hash(regressor1_fit) != hash(regressor2)