[major] speed up nested additive model

[LeonieFreisinger]

🔬 Background

The first implementation includes two forward() passes and hence unnecessary many function calls. --> Objective: Reduce computation time.

🔮 Key changes

📋 Review Checklist

• I have performed a self-review of my own code.
• I have commented my code, added docstrings and data types to function definitions.
• I have added pytests to check whether my feature / fix works.

Please make sure to follow our best practices in the Contributing guidelines.

[codecov-commenter]

Codecov Report

Merging #1311 (60015fd) into main (209129e) will increase coverage by 0.03%.
The diff coverage is 97.43%.

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@@            Coverage Diff             @@
##             main    #1311      +/-   ##
==========================================
+ Coverage   89.84%   89.87%   +0.03%     
==========================================
  Files          38       38              
  Lines        5149     5126      -23     
==========================================
- Hits         4626     4607      -19     
+ Misses        523      519       -4     

Impacted Files	Coverage Δ
neuralprophet/time_dataset.py	94.56% <90.90%> (+0.18%)	⬆️
neuralprophet/components/trend/static.py	81.81% <100.00%> (ø)
neuralprophet/time_net.py	91.07% <100.00%> (+0.51%)	⬆️

[ourownstory]

Great work!! Amazing!!

We can single-track the compute_components later.

[github-actions]

Model Benchmark

Benchmark	Metric	main	current	diff
AirPassengers	MAE_val	13.0626	13.0626	0.0%	✅
AirPassengers	RMSE_val	15.9452	15.9452	0.0%	✅
AirPassengers	Loss_val	0.00131	0.00131	0.0%	✅
AirPassengers	MAE	9.88156	9.88156	0.0%	✅
AirPassengers	RMSE	11.7354	11.7354	0.0%	✅
AirPassengers	Loss	0.00052	0.00052	0.0%	✅
AirPassengers	time	8.43563	8.33	-1.25%	✅
PeytonManning	MAE_val	0.58159	0.58159	0.0%	✅
PeytonManning	RMSE_val	0.72216	0.72216	0.0%	✅
PeytonManning	Loss_val	0.01239	0.01239	0.0%	✅
PeytonManning	MAE	0.41671	0.41671	0.0%	✅
PeytonManning	RMSE	0.55961	0.55961	0.0%	✅
PeytonManning	Loss	0.00612	0.00612	0.0%	✅
PeytonManning	time	19.3805	19.17	-1.09%	✅
YosemiteTemps	MAE_val	1.3442	1.3442	0.0%	✅
YosemiteTemps	RMSE_val	2.00245	2.00245	0.0%	✅
YosemiteTemps	Loss_val	0.00077	0.00077	0.0%	✅
YosemiteTemps	MAE	1.3192	1.3192	0.0%	✅
YosemiteTemps	RMSE	2.13518	2.13518	0.0%	✅
YosemiteTemps	Loss	0.00064	0.00064	0.0%	✅
YosemiteTemps	time	89.6421	91.76	2.36%	✅

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers