Episode 37 — AR, MA, and ARIMA: Choosing the Right Time Series Family
This episode explains autoregressive and moving-average models as tools for capturing temporal dependence, focusing on when each family is appropriate rather than on equations. You will define AR models as using past values to predict future values and MA models as using past errors to correct predictions, then connect these ideas to intuition about momentum and shock correction. ARIMA will be introduced as combining AR, MA, and differencing to handle non-stationary series with trends. You will practice recognizing scenario cues like “dependence on recent history,” “mean reversion,” or “trend present,” and choosing the family that aligns to those properties. Troubleshooting considerations include overfitting with too many lags, misinterpreting noise as signal, and failing to difference appropriately. Real-world examples include forecasting traffic, load, and event counts over time. By the end, you will be able to select time series families in exam questions based on data behavior rather than name recognition. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
