Created by Dr. Pedram Jahangiry | Enhanced with Claude
Interactive Teaching Tool for Understanding Level-Only Exponential Smoothing
The forecast for any future period is simply the current level
Level at time t is a weighted average of current observation and previous level
Smooth & Stable: Creates very smooth forecasts that change slowly. The model gives more weight to historical data, making it resistant to noise but slow to adapt to genuine changes.
Responsive & Reactive: Quickly adapts to recent changes in the data. The model prioritizes recent observations, making forecasts more volatile but responsive to new patterns.
When optimal α is very high (>0.95), the model is essentially saying "I can only trust the most recent observation." This typically indicates that Simple Exponential Smoothing is inadequate for your data—likely because the data contains trend or seasonal patterns that SES cannot capture. Time to consider Holt's or Holt-Winters methods!
The α parameter controls the exponential decay of historical weights. With α = 0.3, the current observation gets 30% weight, the previous gets 21% (0.7 × 0.3), the one before gets 14.7% (0.7² × 0.3), and so on. This creates the characteristic "exponential smoothing" effect where influence decreases exponentially with age.