What are P, D, Q parameters in ARIMA model

The ARIMA model is a statistical model used for time series analysis and forecasting. It stands for Autoregressive Integrated Moving Average, and it requires three parameters to be specified:

1. p (the autoregressive order): This parameter specifies the number of lagged values of the dependent variable that will be used to predict the current value. For example, if p=2, the current value will be predicted based on the two previous values.
2. d (the differencing order): This parameter specifies the number of times the data needs to be differenced to make it stationary. Stationary data has a constant mean and variance over time, and is easier to model. If d=0, the data is already stationary.
3. q (the moving average order): This parameter specifies the number of lagged errors (residuals) that will be used to predict the current value. A moving average model predicts the current value based on the average of the past errors.

In summary, the parameters of the ARIMA model are used to determine the number of autoregressive, differencing, and moving average terms needed to create a model that can accurately predict future values of a time series.