SARIMA

SARIMA

SARIMA

Description

  • SARIMA is an abbreviation for Seasonal Autoregressive Integrated Moving Average.
  • It is an extension of ARIMA used to model seasonal time series.
  • SARIMA considers the events occurring at regular intervals and impacting the target variable similarly every time.
  • Seasonality is the recurrence of impact-causing events at a fixed frequency.

Why to use

SARIMA is used to model seasonal time series

When to use

To model seasonal time-series data

When not to use

When the data does not contain seasonal factor

Prerequisites

Time-series data should not contain null or missing values.

Input

A time-series data with seasonality

Output

  • Forecasting Chart
  • Predicted values with Standard Error

Statistical Methods Used

  • Average
  • Root Mean Square Error

Limitations

--


ARIMA supports data with a trend but no seasonality. SARIMA explicitly handles the seasonal component in the univariate data. Thus, SARIMA effectively forecasts time series with univariate data containing trends and seasonality. While applying SARIMA, the hyperparameters of both the trend and seasonal elements are configured. These are,

  • Trend Elements: p, d, and q
  • Seasonal Elements: P, D, and Q

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