[docs] fix typos

.github/ISSUE_TEMPLATE/bug_report.md

@@ -13,11 +13,11 @@ about: Create a report to help us improve
       cd neural_prophet
       pip install .
       ```
-    * Checked the Answered Questions on the Github Disscussion board: https://github.com/ourownstory/neural_prophet/discussions
+    * Checked the Answered Questions on the Github Discussion board: https://github.com/ourownstory/neural_prophet/discussions
       If you have the same question but the Answer does not solve your issue, please continue the conversation there.
     * Checked that your issue isn't already filed: https://github.com/ourownstory/neural_prophet/issues
       If you have the same issue but there is a twist to your situation, please add an explanation there.
-    * Considered whether your bug might actually be solveable by getting a question answered: 
+    * Considered whether your bug might actually be solvable by getting a question answered: 
       * Please [post a package use question](https://github.com/ourownstory/neural_prophet/discussions/categories/q-a-get-help-using-neuralprophet)
       * Please [post a forecasting best practice question](https://github.com/ourownstory/neural_prophet/discussions/categories/q-a-forecasting-best-practices)
       * Please [post an idea or feedback](https://github.com/ourownstory/neural_prophet/discussions/categories/ideas-feedback)
@@ -48,7 +48,7 @@ Describe what happens, and how often it happens.
 
 If applicable, add screenshots and console printouts to help explain your problem.
 
-**Environement (please complete the following information):**
+**Environment (please complete the following information):**
 
  - Python environment [e.g. Python 3.8, in standalone venv with no other packages]
  - NeuralProphet version and install method [e.g. 2.7, installed from PYPI with `pip install neuralprophet`]

.github/ISSUE_TEMPLATE/feature_request.md

@@ -7,7 +7,7 @@ about: Suggest an idea for this project
 **Prerequisites**
 
 * [ ] Put an X between the brackets on this line if you have done all of the following:
-    * Checked the Answered Questions on the Github Disscussion board: https://github.com/ourownstory/neural_prophet/discussions
+    * Checked the Answered Questions on the Github Discussion board: https://github.com/ourownstory/neural_prophet/discussions
       If you have the same question but the Answer does not solve your issue, please continue the conversation there.
     * Checked that your issue isn't already filed: https://github.com/ourownstory/neural_prophet/issues
       If you have the same issue but there is a twist to your situation, please add an explanation there.

README.md

@@ -100,7 +100,7 @@ pip install .
 
 
 ### Framework features
-* Multiple time series: Fit a global/glocal model with (partially) shared model parameters
+* Multiple time series: Fit a global/local model with (partially) shared model parameters
 * Uncertainty: Estimate values of specific quantiles - Quantile Regression
 * Regularize modelling components
 * Plotting of forecast components, model coefficients and more

docs/source/how-to-guides/feature-guides/hyperparameter-selection.md

@@ -21,7 +21,7 @@ NeuralProphet is fit with stochastic gradient descent - more precisely, with an
 If the parameter `learning_rate` is not specified, a learning rate range test is conducted to determine the optimal learning rate. 
 The `epochs`, `loss_func` and `optimizer` are other parameters that directly affect the model training process. 
 If not defined, `epochs`and `loss_func` are automatically set based on the dataset size. They are set in a manner that controls the total number training steps to be around 1000 to 4000.
-NeuralProphet offers to set two different values for `optimizer`, namely `AdamW` and `SDG` (stochastic gradient decsent).
+NeuralProphet offers to set two different values for `optimizer`, namely `AdamW` and `SDG` (stochastic gradient descent).
 
 If it looks like the model is overfitting to the training data (the live loss plot can be useful hereby), 
 you can reduce `epochs`  and `learning_rate`, and potentially increase the `batch_size`.