Exponential Smoothing provides a way to incorporate more structure into forecasts by assigning exponentially decreasing weights to past observations. This weighting scheme allows the model to be more responsive to recent data while still retaining information from the past.
These methods are especially effective for Time Series Forecasting that exhibit patterns such as: Level (baseline average), Trend (direction of change), Seasonality (regular fluctuations over time).
Exponential Smoothing is a family of forecasting methods where forecasts are generated by applying weights that decrease exponentially as observations get older.
- Recent observations have the highest impact on the forecast.
- Older observations are never fully discarded but contribute less over time.
- This makes the method flexible for adapting to new trends or shifts in the data.
When to Use Each Method
Qu: What do the parameters for each model tell us?
- SES → Data with no trend/seasonality (e.g., stationary demand).
- Holt’s → Data with trend but no seasonality (e.g., stock with upward drift).
- Holt-Winters → Data with both trend and seasonality (e.g., sales data with monthly cycles).
All three methods can be implemented using the holtwinters (or statsmodels) package in Python.
from statsmodels.tsa.holtwinters import ExponentialSmoothing