Autocorrelated Data and Dynamic Systems

Scott Pardo ()
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Scott Pardo: Ascensia Diabetes Care, Global Medical & Clinical Affairs

Chapter Chapter 15 in Statistical Analysis of Empirical Data, 2020, pp 197-207 from Springer

Abstract: Abstract Dynamic systems are represented by variables that change in time, and are related to their values in the past. Linear time series models provide a framework for fitting dynamic data. The most important feature exploited by these models is called the autocorrelation (or partial autocorrelation) function. These functions can be estimated under certain regularizing conditions, known as stationarity. When the data-generating process is stationary, the estimates of the auto- and partial autocorrelation functions can be used to suggest a model.

Keywords: Time series; Autocorrelation; Autoregressive; Moving average; ARIMA (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-43328-4_15

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DOI: 10.1007/978-3-030-43328-4_15

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