[typing] Add ConfigEvents type annotations

[noxan]

Close #953

[github-actions]

e592381

Model Benchmark

Benchmark	Metric	main	current	diff
YosemiteTemps	MAE_val	1.72949	1.72949	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%	✅
YosemiteTemps	time	93.44	93.4	-0.04%	✅
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%	✅
PeytonManning	time	11.72	11.7	-0.17%	✅
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%	✅
AirPassengers	time	4.51	4.51	0.0%	✅

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers

[codecov-commenter]

Codecov Report

Merging #989 (4df76c3) into main (18e206b) will not change coverage.
The diff coverage is 66.66%.

@@           Coverage Diff           @@
##             main     #989   +/-   ##
=======================================
  Coverage   89.19%   89.19%           
=======================================
  Files          18       18           
  Lines        4450     4450           
=======================================
  Hits         3969     3969           
  Misses        481      481           

Impacted Files	Coverage Δ
neuralprophet/configure.py	89.81% <ø> (ø)
neuralprophet/df_utils.py	94.72% <50.00%> (ø)
neuralprophet/utils.py	80.06% <66.66%> (ø)
neuralprophet/time_dataset.py	94.44% <100.00%> (ø)

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[karl-richter]

Looks good to me