How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls

In my other posts, I have covered topics such as: How to combine machine learning and physics, and how machine learning can be used for production optimization as well as anomaly detection and condition monitoring. But in this post, I will discuss some of the common pitfalls of machine learning for time series forecasting. Time series forecasting is an important area of machine learning. It is important because there are so many prediction problems that involve a time component. However, while the time component adds additional information, it also makes time series problems more difficult to handle compared to many other prediction tasks.