[Suggestion] Normalization.

Adding normalization to the data preprocessor might be a great feature:

- Min-Max Normalization,
- Decimal Scaling Normalization,
- Z-Score Normalization,
- Median Normalization,
- Sigmoid Normalization,
- Tanh estimators

- Bhanja, Samit & Das, Abhishek. (2018). Impact of Data Normalization on Deep Neural Network for Time Series Forecasting. [ResearchGate](https://www.researchgate.net/publication/329641742_Impact_of_Data_Normalization_on_Deep_Neural_Network_for_Time_Series_Forecasting)

These are more advanced / adaptive approaches:

- Passalis, Nikolaos, u. a. Deep Adaptive Input Normalization for Time Series Forecasting. 2019. [Github Repo](https://github.com/breznak/dain) [arXiv:1902.07892](https://arxiv.org/abs/1902.07892)
- Nalmpantis, Angelos, u. a. „Deep Adaptive Group-Based Input Normalization for Financial Trading“. Pattern Recognition Letters, Bd. 152, Dezember 2021, S. 413–19. DOI.org (Crossref), https://doi.org/10.1016/j.patrec.2021.11.004
- Tran, Dat Thanh, u. a. Bilinear Input Normalization for Neural Networks in Financial Forecasting. 2021. [arXiv:2109.00983](https://arxiv.org/abs/2109.00983)