What are the best techniques for "Feature Engineering" in Time-Series Predictive Analytics?

All answers to this question.

For energy forecasting, "Lag Features" (e.g., consumption at T-24 hours) are usually the most predictive. However, to capture the cyclical nature, you should transform your time variables using "Cyclical Encoding" with Sine and Cosine functions. This ensures that hour 23 and hour 0 are treated as being close together, which a standard linear scale fails to do. For holidays, create a "Boolean" feature or use a specialized library like Prophet that has built-in holiday effects. Rolling statistics, like a 7-day moving average of temperature, also help the model understand the broader context beyond just the instantaneous data point.

   Answered 2024-09-22 by Michelle Garcia

Have you tried using "Exogenous Variables" like local weather forecasts or economic indicators to see if they correlate with your energy consumption spikes?

   Answered 2024-09-24 by Robert Wilson

• Robert, we just added "Heating Degree Days" (HDD) as an exogenous feature, and our MAPE dropped by nearly 3%. It turns out that a 2-degree drop in outside temperature has a non-linear impact on our grid load that the model couldn't infer from just time data alone. We are now looking into integrating a real-time weather API into our data pipeline so the model can adjust its 48-hour outlook dynamically. It’s been a game-changer for our peak-load management strategies and has saved the company a lot in energy procurement.

     Commented 2024-04-25 by David Foster

Fourier Transforms are great if you have multiple overlapping seasonalities, like a 24-hour cycle and a 12.4-hour tidal cycle, but they can be overkill for basic energy use.

   Answered 2024-09-26 by Patricia Moore

• Patricia is right. Keep it simple first. Most business problems can be solved with well-crafted lag features and basic calendar-based categorical variables.

     Commented 2024-09-27 by Christopher Lee