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Understanding Stationary Time Series by Carmen Orazzo is a document available to read on EtoBox.
What is Understanding Stationary Time Series about?
This document discusses stationary and non-stationary time series. It defines a stationary time series as having a constant mean, variance, and covariance over time. Examples provided include white noise (WN), moving average (MA), and autoregressive (AR) processes. Non-stationary examples provided have means or variances that change over time. Autocorrelation (ACF) and partial autocorrelation (PACF) functions are discussed to help determine if a stationary series is white noise or another type of process.
- Author
- Carmen Orazzo
- Language
- EN