ARIMA

ARIMA
Description	ARIMA stands for Auto-Regressive Integrated Moving Average. It is used for forecasting on stationary data.
Why to use	ARIMA captures both autoregressive and moving average components, which provides a systematic way to deal with non-stationary data by differencing. Also it is effective for predicting future values based on past trends and patterns in the data.
When to use	When you have a stationary or near stationary series. when there is significant autocorrelation in the data	When not to use	On seasonal data Non-linear relationship in data High frequency noise in data
Prerequisites	The data should be stationary.
Input	Any time series data.	Output	Forecasting Chart Trained model parameters Accuracy
Statistical Methods Used	Moving averages	Limitations	Non-linear data Sensitivity to outliers Non-stationarity of data

ARIMA or Autoregressive Integrated Moving Average is an extension of ARMA model. The ARIMA model can handle non-stationary data, that is time dependent data more effectively. ARIMA converts the non-stationary data into stationary data by performing differencing.

Differencing refers to subtracting the previous observation from the current observations. The differencing is performed until the data becomes stationary.