SARIMA/SARIMAX

Install and import

bash

npm install fintech-algorithms

import { forecastSARIMAX } from "fintech-algorithms/statistical-time-series/forecast-models/sarima-sarimax";

Signature

forecastSARIMAX(values, exog, futureExog, beta, ar, ma, seasonalAr, seasonalMa, intercept, differenceOrder, seasonalDifferenceOrder, period, horizon)

Worked example

executed Captured by running this function on the input its own test provides. Real output of real code — but not asserted against a published figure.

Input

values

[12, 15, 18, 21, 24, 27]

Showing 6 of 8 elements.

exog

[
  [1],
  [2],
  [3]
]

Showing 3 of 8 elements.

futureExog

[
  [9],
  [10]
]

beta

[2]

[0.5]

[]

seasonalAr

[0.25]

seasonalMa

[]

intercept

differenceOrder

seasonalDifferenceOrder

period

horizon

Call

forecastSARIMAX(values, exog, futureExog, beta, ar, ma, seasonalAr, seasonalMa, intercept, differenceOrder, seasonalDifferenceOrder, period, horizon)

Returns

object with 6 fields: forecast, fitted, residuals, state, transformed_forecast, residualized_forecast

{
  "forecast": [35.625, 38.0625],
  "fitted": [null, null, null, 0.625, 0.625, 0.625],
  "residuals": [0, 0, 0, 0.375, 0.375, 0.375],
  "state": {
    "order": [3, 0],
    "last_values": [1, 1, 1],
    "last_residuals": [0.375],
    "presample_innovations": "zero",
    "future_innovations": "zero conditional mean",
    "orders": {
      "nonseasonal": [1, 1, 0],
      "seasonal": [1, 0, 0, 2]
    },
    "combined_ar": [0.5, 0.25, -0.125],
    "combined_ma": [],
    "beta": [2]
  },
  "transformed_forecast": [0.625, 0.4375],
  "residualized_forecast": [17.625, 18.0625]
}

Diagrams

How it works

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References

statsmodels.tsa.statespace.sarimax.SARIMAX — statsmodels developers
Forecasting: Principles and Practice — Seasonal ARIMA — Rob J. Hyndman and George Athanasopoulos
statsmodels.tsa.arima.model.ARIMA — statsmodels developers
Forecasting: Principles and Practice — Evaluating point forecast accuracy — Rob J. Hyndman and George Athanasopoulos
Forecasting: Principles and Practice — Time series cross-validation — Rob J. Hyndman and George Athanasopoulos
Forecasting: Principles and Practice — Evaluating regression and residual behavior — Rob J. Hyndman and George Athanasopoulos