Exponential Smoothing

Overview

Exponential smoothing assigns exponentially decreasing weights to past observations. Three variants: Simple (SES, level only), Holt (level + trend), Holt-Winters (level + trend + seasonality). ETS framework (Error-Trend-Seasonality) provides a unified statistical model. Fast, interpretable, and competitive with complex models for short horizons.

When to Use

Trigger conditions:

• Quick forecasting with minimal configuration
• Short-horizon forecasts (1-2 seasonal cycles ahead)
• Data with clear level, trend, and/or seasonal components

When NOT to use:

• For long-range forecasts (uncertainty accumulates too fast)
• When external regressors are important (use regression or ML models)

Algorithm