Auto ARIMA

Auto ARIMA

Auto ARIMA

Description

Auto ARIMA (Auto-Regressive Integrated Moving Average) is a statistical algorithm that uses time series data to forecast future values. It automatically determines the best parameters for an ARIMA model.

Why to use

Auto ARIMA automates the selection of ARIMA parameters, saving time and effort compared to manual tuning. It uses criteria like AIC or BIC to evaluate models, providing a systematic approach to model selection.

When to use

  • When you have time series data with unknown parameters

When not to use

  • Seasonal Data

Prerequisite

Stationarity

Input

  • Time series data

Output

  • Forecasting chart with predicted value.
  • Trained Model Parameters
  • Accuracy Parameters

Statistical Methods

  • AIC / BIC
  • Autocorrelation analysis

Limitations

  • Computationally extensive
  • Non-stationary data

The Auto ARIMA method is an automated approach for fitting ARIMA model to a time series by finding best set of parameters without any manual intervention.
Auto ARIMA conducts differencing tests for determining the order of differencing and then fits the model with in defined ranges. Its ability to compare model using various criteria simplifies the process of identifying the best fit model.

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