[refactor] Encapsulate _prepare_dataframe_to_predict

[LeonieFreisinger]

🔬 Background

Related to #1133
This PR is part of a series of PRs belonging to step 2 of restructuring the forecaster.py. Step 1 was to move identified functions out of the forecaster.py into dedicated files. Step 2 is to encapsulate the input arguments of the moved functions. Step2 will be implemented step-by-step within multiple PRs

🔮 Key changes

• Encapsulate _prepare_dataframe_to_predict including docstring and typing. Model still needs to for passing on to _check_dataframe

📋 Review Checklist

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

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

[github-actions]

Model Benchmark

Benchmark	Metric	main	current	diff
AirPassengers	MAE_val	13.0626	13.0626	-0.0%	✅
AirPassengers	RMSE_val	15.9453	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	6.70212	7.95	18.62%	❌
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	82.4342	98.93	20.01%	❌
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	15.8784	19.14	20.54%	❌

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers

[codecov-commenter]

Codecov Report

Merging #1290 (01a0e17) into main (05b093c) will not change coverage.
The diff coverage is 80.00%.

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@@           Coverage Diff           @@
##             main    #1290   +/-   ##
=======================================
  Coverage   89.85%   89.85%           
=======================================
  Files          38       38           
  Lines        5128     5128           
=======================================
  Hits         4608     4608           
  Misses        520      520           

Impacted Files	Coverage Δ
neuralprophet/data/process.py	92.46% <75.00%> (ø)
neuralprophet/forecaster.py	87.38% <100.00%> (ø)

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[noxan]

LGTM