Crafting Time Series Features for Better Models
Time series data contains rich temporal information that standard tabular methods often overlook. Careful feature engineering can reveal trends and cycles that lead to more accurate predictions.
1. Lagged Variables
One of the simplest yet most effective techniques is creating lag features. By shifting the series backward in time, you supply the model with previous observations that may influence current values.
2. Rolling Statistics
Moving averages and rolling standard deviations smooth the data and highlight short-term changes. They help capture momentum and seasonality without introducing noise.
3. Seasonal Indicators
Adding flags for month, day of week, or other periodic markers enables models to recognize recurring patterns, improving forecasts for sales, web traffic, and more.
Combining these approaches can significantly enhance a time series model’s predictive power, especially when paired with algorithms like ARIMA or gradient boosting.
