Simple Baseline Methods#
Easy reference forecasts (last value, mean) that stronger models must beat.
Important
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What they are#
Simple baseline methods are a small family of trivially simple forecasts used to set a statistical baseline before anything complex. The staples: the mean method (forecast the historical average, which smooths away seasonality), the naïve method (carry the last value forward — a random walk), the seasonal naïve (carry the value from one season ago), and the drift method (naïve plus a trend equal to the average historical change).
The drift formula#
equivalent to drawing a line through the first and last observations and extrapolating it forward.
Why use them#
They are cheap and transparent, and any sophisticated model must beat them to earn its complexity. Choose by structure — the mean for a flat series, the naïve for a random walk, the seasonal naïve for strong seasonality, drift for a trend — and note that averaging several often improves accuracy. KISS: keep it sophisticatedly simple.
Theme: Signal Processing & Time Series · All terminology
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Mind map — connected ideas
Naïve Baseline Forecast · Forecasting Benchmarks · Seasonal Lag · Forecast Error · Time Series Forecasting · Forecasting Competitions
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More in Signal Processing & Time Series
ARIMA (AutoRegressive Integrated Moving Average) · Bayesian Time Series · Forecast Error · Forecasting Benchmarks · Forecasting Competitions · Log-Space · Low-pass Filtering · LSTM — Long Short-Term Memory Networks · M-Competitions (Makridakis Competitions) · Naïve Baseline Forecast · Prophet — Time Series Forecasting by Facebook (Meta) · Seasonal Lag · Seasonality · Signal Processing
See also
Source article Adapted (context, re-expressed) in our own words from: Simple Baseline Methods (insightful-data-lab.com).