Therefore, an amount of data with recent periods needed to be selected as the validation set first, and then other processes can be carried on like usual.

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Reference

Aishwarya S. (2019). 6 Powerful Feature Engineering Techniques For Time Series Data (using Python). Retrieved from https://www.analyticsvidhya.com/blog/2019/12/6-powerful-feature-engineering-techniques-time-series/

Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142.

Brown, R. G. (2004). Smoothing, forecasting and prediction of discrete time series. Courier Corporation.

Mishtert, T. (2019). Detecting Suspicious Timestamp In a Transfer or a Transaction Data. Retrieved from https://medium.com/@mishtert/detecting-suspicious-timestamp-analyzing-time-circular-histogram-e0b2e747e9bd

Stephanie G. (2019). Von Mises Distribution: Simple Definition & Examples. Retrieved from https://www.statisticshowto.com/von-mises-distribution/

Vegard F. (2019). How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls. Retrieved from https://www.kdnuggets.com/2019/05/machine-learning-time-series-forecasting.html

Vegard F. (2019). How (not) to use Machine Learning for time series forecasting: The sequel. Retrieved from https://www.kdnuggets.com/2020/03/machine-learning-time-series-forecasting-sequel.html#:~:text=Time%20series%20forecasting%20is%20an%20important%20area%20of%20machine%20learning.&text=However%2C%20while%20the%20time%20component,to%20many%20other%20prediction%20tasks