TSA-ARIMA-Tool
More information:
ARIMA(Autoregressive Integrated Moving Average) is used for analyzing and forecasting time series data. It combines the concepts of autoregression (AR), differencing (I), and moving average (MA) to model the behavior of a time series.
The ARIMA model is defined by three main parameters: p (order of autoregression): The number of lagged observations included in the model. d (degree of differencing): The number of times the raw observations are differenced to make the time series stationary. q (order of moving average): The size of the moving average window.
For the seasonal time series data, besides p, d and q, it needs the P, D and Q parameters for seasonal model.