I have a time series dataset with the following characteristics:
- 5 years duration data
- 50 features in time-series, daily (temperature, humidity, etc)
- Target: predict CO2 level
I'm looking for a time-series multi-variate algorithm to help me predict the CO2 of tomorrow, having temperature, humidity, etc of today. All of these features have their own patters and might/might not be related with CO2.
Nevertheless, what kind of model would you recommend (e.g., RNN) in Python? How would I fit this amount of features to the predictor? How would I define the training window size?