ARIMA Model: Time Series Analysis and Forecasting

ARIMA, or Autoregressive Integrated Moving Average, is a statistical model used for time series analysis and forecasting. It combines three components: autoregression (AR), differencing (I), and moving average (MA).

• Autoregression (AR) refers to a model that uses the past values of a dependent variable to predict its future values.
• Differencing (I) is used to remove the trend or seasonality in the time series data.
• Moving Average (MA) is a model that uses the past errors to predict future values.

ARIMA models are often used to forecast economic and financial data, as well as in areas like weather forecasting, demand forecasting, and stock price forecasting. They can be applied to both stationary and non-stationary time series data. The parameters of an ARIMA model can be estimated using statistical techniques such as maximum likelihood estimation or Bayesian estimation.