Statistical Methods for Data Science
The ARIMA model, which stands for Autoregressive Integrated Moving Average, is a popular statistical method used for analyzing and forecasting time series data. It combines three components: autoregression (AR), differencing (I), and moving average (MA), making it particularly useful for modeling non-stationary time series that can be transformed into stationary series. Understanding the ARIMA model is crucial for identifying underlying patterns in data, assessing stationarity, and performing accurate forecasts.
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