ARIMA

ARIMA (AutoRegressive Integrated Moving Average) is a time series forecasting model that takes into account the autocorrelation and trend in the data. It is a combination of three components: autoregression (AR), differencing (I), and moving average (MA). ARIMA models are widely used in finance, economics, and other fields where time series data is prevalent. The model is capable of capturing both short-term and long-term trends in the data, making it a powerful tool for forecasting future values. ARIMA models require the data to be stationary, meaning that the mean and variance of the data should be constant over time. If the data is non-stationary, it can be transformed using differencing to make it stationary. ARIMA models can be further extended to include seasonal components, resulting in SARIMA models.
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