TSA-ARIMA-Prewhitening
Prewhitening in time series analysis refers to a technique used to remove autocorrelation from a time series data before applying a particular model or analysis. The goal of prewhitening is to transform the original time series into a white noise series, which has no autocorrelation.
The prewhitening process involves fitting an ARIMA model to the original time series data and then using the residuals (errors) from this model as the prewhitened series. By doing so, the autocorrelation structure in the original data is removed, and the prewhitened series can be considered as a white noise process.
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