Remove residuals in forecast dataframe returned by m.predict()

[judussoari]

*Disabled the plotting option for residuals as well

*Not really needed as residuals are part of basic metrics such as MAE and RMSE

*forecast df is very large, which may overwhelm the user. Now more lean

[codecov-commenter]

Codecov Report

Merging #927 (10748bd) into main (e7a7dbd) will increase coverage by 0.24%.
The diff coverage is 87.50%.

@@            Coverage Diff             @@
##             main     #927      +/-   ##
==========================================
+ Coverage   87.43%   87.68%   +0.24%     
==========================================
  Files          17       17              
  Lines        4466     4433      -33     
==========================================
- Hits         3905     3887      -18     
+ Misses        561      546      -15     

Impacted Files	Coverage Δ
neuralprophet/plot_forecast.py	79.22% <66.66%> (+3.89%)	⬆️
neuralprophet/forecaster.py	87.97% <100.00%> (-0.02%)	⬇️
neuralprophet/plot_forecast_plotly.py	91.37% <100.00%> (+0.65%)	⬆️

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[Kevin-Chen0]

Can you have it pass the flake8 and metrics test? Thanks.

[Kevin-Chen0]

Can you pass the flake8 test by resolving this following issue?

local variable 'notnull' is assigned to but never used

[ourownstory]

In the future we may add a tutorial on how to analyze residuals and what one can learn from them.

[ourownstory]

LGTM

[ourownstory]

fixed merge commits. ready to merge when tests pass.

[github-actions]

169e0b3

Model Benchmark

Benchmark	Metric	main	current	diff
AirPassengers	MAE_val	15.2698	15.2698	0.0%	✅
AirPassengers	RMSE_val	19.4209	19.4209	0.0%	✅
AirPassengers	Loss_val	0.00195	0.00195	0.0%	✅
AirPassengers	RegLoss_val	0	0	0.0%	✅
AirPassengers	epoch	89	89	0.0%	✅
AirPassengers	MAE	9.82902	9.82902	0.0%	✅
AirPassengers	RMSE	11.7005	11.7005	0.0%	✅
AirPassengers	Loss	0.00056	0.00056	0.0%	✅
AirPassengers	RegLoss	0	0	0.0%	✅
PeytonManning	MAE_val	0.64636	0.64636	-0.0%	✅
PeytonManning	RMSE_val	0.79276	0.79276	-0.0%	✅
PeytonManning	Loss_val	0.01494	0.01494	-0.0%	✅
PeytonManning	RegLoss_val	0	0	0.0%	✅
PeytonManning	epoch	37	37	0.0%	✅
PeytonManning	MAE	0.42701	0.42701	0.0%	✅
PeytonManning	RMSE	0.57032	0.57032	-0.0%	✅
PeytonManning	Loss	0.00635	0.00635	0.0%	✅
PeytonManning	RegLoss	0	0	0.0%	✅
YosemiteTemps	MAE_val	1.72949	1.72948	-0.0%	✅
YosemiteTemps	RMSE_val	2.27386	2.27386	-0.0%	✅
YosemiteTemps	Loss_val	0.00096	0.00096	-0.0%	✅
YosemiteTemps	RegLoss_val	0	0	0.0%	✅
YosemiteTemps	epoch	84	84	0.0%	✅
YosemiteTemps	MAE	1.45189	1.45189	-0.0%	✅
YosemiteTemps	RMSE	2.16631	2.16631	-0.0%	✅
YosemiteTemps	Loss	0.00066	0.00066	0.0%	✅
YosemiteTemps	RegLoss	0	0	0.0%	✅

Model Training

PeytonManning

YosemiteTemps

AirPassengers