Feature engineering for time series data
Effective feature engineering is essential in time series analysis to uncover meaningful patterns and enhance predictive accuracy. It involves transforming raw data into informative features that capture temporal dependencies, seasonal variations, and other relevant aspects of the time series. The first technique we are going to explore is creating lags of features.
Lag features and their importance
Lag features are a crucial aspect of time series feature engineering as they allow us to transform time series data into a format suitable for supervised learning models. Lag features involve creating new variables that represent past observations of the target variable:
- Lag 1: The value from the previous time step
- Lag 2: The value from two time steps ago
- Lag k: The value from k time steps ago
By shifting the time series data by a specified number of time steps (referred to as the lag), these past values are included...
