📈 Forecasting Benchmarks

Forecasting Benchmarks#

Standard datasets and baselines for comparing forecasting methods.

Important

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What it is#

A forecasting benchmark is the reference forecast a proposed model must outperform to be worth using. In practice the simple baseline methods (naïve, seasonal naïve, mean, drift) fill this role — the naïve forecast is the standard reference, and the basis of MASE.

The discipline#

Always establish a benchmark before reaching for complex models — a step that is often skipped. Any complex model must be better than the baseline to be considered. A model that only marginally beats the naïve forecast probably is not worth its added complexity and maintenance.

Beyond one series#

Shared datasets (such as the M-competition series) act as community benchmarks, letting different methods be compared on common ground rather than on each author’s private data.

Theme: Signal Processing & Time Series · All terminology

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Mind map — connected ideas

Simple Baseline Methods · Naïve Baseline Forecast · Forecast Error · M-Competitions (Makridakis Competitions) · Relative accuracy · Forecasting Competitions

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More in Signal Processing & Time Series

ARIMA (AutoRegressive Integrated Moving Average) · Bayesian Time Series · Forecast Error · 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 · Simple Baseline Methods