Feature engineering on time series data

Many a times, for use cases such as forecasting a stock’s trend, it’s more beneficial to detect if the stock is going up or down by at least some percent value higher, and then it becomes a classical machine learning problem, especially you want to add in information such as ‘news sentiments’ or ‘expert opinions’ as features.
There are three ways to engineer time series based features:

  1. Build different time series forecasting models and simply use forecasted values as features
  2. Delve more into signal processing, such as computing peaks and troughs, and design features such as average slope since last trough, average trough to peak distance.
  3. Understand the domain, and most important technical indicators trades use, and convert them as features.