Time Series Forecasting (1): Traditional Statistical Models

Sun, 01 Sep 2024 09:00:00 +0000

The first time I touched data that “looked like a time series” — hourly server CPU usage — my instinct was to throw it at a linear regression. Time on the x-axis, usage on the y-axis. The fit was terrible. The problem wasn’t the regression; the problem was that this kind of data has its own personality. It has trends, seasonality, and a stubborn dependence between consecutive observations. A vanilla regression treats every row as an independent sample and throws away the one piece of information that matters most: time itself.

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Time Series Forecasting (1): Traditional Statistical Models