M-N-A

251. M-N-A

SES + add. seasonality, mult. errors

ETS (𝑀, 𝑁, 𝐴)

𝑥_{𝑡} = (𝑙_{𝑡 - 1} + 𝑠_{𝑡 - 𝑚}) (1 + 𝜀_{𝑡})

𝑙_{𝑡} = 𝑙_{𝑡 - 1} + 𝛼 (𝑙_{𝑡 - 1} + 𝑠_{𝑡 - 𝑚}) 𝜀_{𝑡}

𝑠_{𝑡} = 𝑠_{𝑡 - 𝑚} + 𝛾 (𝑙_{𝑡 - 1} + 𝑠_{𝑡 - 𝑚}) 𝜀_{𝑡}

{\hat{𝑥}}_{𝑡 + ℎ | 𝑡} = 𝑙_{𝑡} + 𝑠_{𝑡 + ℎ - 𝑚_{ℎ}^{+}}

Example:

ETS (𝑀, 𝑁, 𝐴)

Given

Smoothing parameters: $𝛼 = 0.5$ , $𝛾 = 0.2$
Initial states: $𝑙_{0} = 12$ , $(𝑠_{- 3}, 𝑠_{- 2}, 𝑠_{- 1}, 𝑠_{0}) = (2, 0, - 3, 1)$ , seasonal period $𝑚 = 4$
Data:

$𝑡$	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
$x_{𝑡}$	12	10	8	11	14	12	9	13	16	14	11	15	18	16	13	17

Step 1 — formula

Observation:

𝑥_{𝑡} = (𝑙_{𝑡 - 1} + 𝑠_{𝑡 - 𝑚}) (1 + 𝜀_{𝑡})

Conditional mean (one-step-ahead forecast ${\hat{𝑥}}_{𝑡 | 𝑡 - 1} = 𝜇_{𝑡}$ ):

𝜇_{𝑡} = 𝑙_{𝑡 - 1} + 𝑠_{𝑡 - 𝑚}

Innovation:

𝜀_{𝑡} = (𝑥_{𝑡} - 𝜇_{𝑡}) / 𝜇_{𝑡}

State updates:

𝑙_{𝑡} = 𝑙_{𝑡 - 1} + 𝛼 𝜇_{𝑡} 𝜀_{𝑡}

𝑠_{𝑡} = 𝑠_{𝑡 - 𝑚} + 𝛾 𝜇_{𝑡} 𝜀_{𝑡}

Forecast $ℎ$ steps ahead from time $𝑡$ (using current-period states):

{\hat{𝑥}}_{𝑡 + ℎ | 𝑡} = 𝑙_{𝑡} + 𝑠_{𝑡 + ℎ - 𝑚_{ℎ}^{+}}

where $𝑚_{ℎ}^{+} = ((ℎ - 1) mod 𝑚) + 1$ picks the right seasonal slot for the period $ℎ$ steps ahead (cycles through $1, 2, \dots, 𝑚$ ).

Step 2 — apply at $𝑡 = 1$

𝜇_{1} = 12 + 2 = 14

𝜀_{1} = (𝑥_{1} - 𝜇_{1}) / 𝜇_{1} = (12 - 14) / 14 = - 0.1429

𝑙_{1} = 12 + 0.5 \cdot 14 \cdot (- 0.1429) = 11

𝑠_{1} = 2 + 0.2 \cdot 14 \cdot (- 0.1429) = 1.6

Step 3 — iterate

Each column header is the equation that produced its values. Values rounded to 4 decimal places; arithmetic performed at full precision.