ets – Exponential Smoothing State-Space (Hyndman-Koehler-Ord-Snyder 2008) – ETS family.#

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Operational op under axis family, sub-layer L4_A_model_selection, layer l4. Standalone callable: mf.functions.ets_fit.

Function signature#

mf.functions.ets_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
) -> ETSFitResult

Parameters#

name

type

default

constraint

description

X

`np.ndarray

pd.DataFrame`

—

—

y

`np.ndarray

pd.Series`

—

—

Returns#

ETSFitResult — frozen dataclass with fit results.

Attribute

Type

Description

.error_trend_seasonal

str

3-character ETS code, e.g. AAN.

.n_obs

int

Number of observations.

.predict(X)

np.ndarray

Forecast, len(X) steps ahead.

.summary()

str

Table: ETS code and observation count.

Behavior#

Exponential-smoothing state-space framework: error_trend_seasonal is a 3-character code ETS where E ∈ {A, M} (additive / multiplicative error), T ∈ {A, M, N} (additive / multiplicative / no trend), S ∈ {A, M, N} (additive / multiplicative / no seasonality). Wraps statsmodels.tsa.exponential_smoothing.ets.ETSModel (MLE fitting; auto-selects the closed-form initialisation per Hyndman 2008 §5.4).

Defaults: error_trend_seasonal = 'AAN' (additive error, additive trend, no seasonal – the workhorse non-seasonal spec), seasonal_periods = 12 (monthly), initialization_method = 'estimated'. Auto-disables seasonal fitting when len(y) < 2 · seasonal_periods.

When to use

M-competition baseline; non-seasonal / seasonal univariate forecasting where a state-space exponential-smoothing model is the natural reference.

When NOT to use

Multivariate or covariate-driven forecasting (ETS ignores X); short series where seasonal estimation is unstable.

In recipe context#

Set params.family = "ets" in the relevant layer to activate this op within a recipe:

# Layer L4 recipe fragment
params:
  family: ets

References#

  • macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

  • Hyndman, Koehler, Ord & Snyder (2008) ‘Forecasting with Exponential Smoothing: The State Space Approach’, Springer.

  • Hyndman & Athanasopoulos (2018) ‘Forecasting: Principles and Practice’, 2nd ed., OTexts §7.