← All methods

Smoothing · Free

Simple exponential smoothing

A fading memory of the local level — no trend, no season.

What it assumes

Local level only; no trend, no season.

When to use

Stable or slowly changing mean; intermittent-adjacent smooth series.

When to avoid

Trend or seasonality. On spare parts with zeros, prefer Croston.

Knobs

l_t = α y_t + (1−α) l_{t−1}. α is grid-searched on SSE.

How it works

Simple exponential smoothing updates a level: ℓ_t = α y_t + (1−α) ℓ_{t−1}. The forecast is that level for every horizon. α near 1 trusts the latest point (almost naive); α near 0 is very smooth. The engine grid-searches α on SSE. SES is the workhorse for a stable mean. Trend or season need Holt or Holt–Winters; many zeros need the Croston family.

Sample forecast question

Given these eight Monday call counts, what is the simple-exponential-smoothing forecast for the next three Mondays?

Step-by-step on these numbers

Sample series (calls)
PeriodActualFitted
M11919
M22119
M31819.40
M42019.12
M52219.30
M61919.84
M72019.67
M82119.74
Forecast
PeriodForecast95% interval
M919.9917.28 – 22.70
M1019.9916.15 – 23.82
M1119.9915.29 – 24.69

Parameters the engine found

  • alpha = 0.200
  • last = 19.988
  1. The question

    Given these eight Monday call counts, what is the simple-exponential-smoothing forecast for the next three Mondays? Sample mondays: M1=19, M2=21, M3=18, M4=20, M5=22, M6=19, M7=20, M8=21 (calls).

  2. Level recurrence

    ℓ_t = α y_t + (1−α) ℓ_{t−1}, with ℓ_0 = y_1 = 19. The h-step forecast is the last level.

  3. Pick α

    The engine grid-searches α ∈ {0.05, 0.10, …, 0.95} on in-sample SSE. Winner: α = 0.200.

  4. First updates

    t=1: forecast 19 against y=19, then ℓ = 0.200×19 + 0.800×19 = 19. t=2: forecast 19 against y=21, then ℓ = 0.200×21 + 0.800×19 = 19.40. t=3: forecast 19.40 against y=18, then ℓ = 0.200×18 + 0.800×19.40 = 19.12.

  5. Last level

    After the full sample, ℓ_n = 19.99 (engine last = 19.99). Forecast: M9: 19.99; M10: 19.99; M11: 19.99.

  6. Interval

    Residual σ = 1.384. For h = 1 the 95% band is [17.28, 22.70] around 19.99. The engine labels this interval native for the method.

Graph of this sample

M1 · 19 M8 M9
Simple exponential smoothing on the canned sample: actuals, fitted, and a 3-step forecast. Actual Fitted Forecast
The chart plots the canned table on this page (actuals, in-sample fitted, forecast, 95% interval). Not your Excel series. Free analysis never uploads raw data.

Educational only. Not investment, weather, or operational advice. In the add-in, rank is rolling-origin MASE — “best supported among candidates on this series.”

Use it in the Excel Add-in