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.