Intro to Time Series
ARIMA models, or AutoRegressive Integrated Moving Average models, are a class of statistical methods used for analyzing and forecasting time series data. They combine three key components: autoregression (AR), differencing (I), and moving averages (MA), making them versatile in capturing various patterns in data, including trends and seasonality. These models are particularly useful for transforming non-stationary time series into stationary ones through differencing, and they help in understanding cyclical and irregular components in the data.
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