Titlebook: Applied Time Series Analysis and Forecasting with Python; Changquan Huang,Alla Petukhina Textbook 2022 The Editor(s) (if applicable) and T

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Time Series Concepts and Python, and objectives of time series analysis. We introduce the programming language Python and its extension packages and demonstrate some useful usages in the field of time series. We also introduce the concept of stationarity and two important time series models: white noise and random walk. At last, w

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Stationary Time Series Models,nary time series stationary. Then we present a statistical test on stationarity—the KPSS stationarity test. Third, we define MA, AR, and ARMA models and discuss their properties, including invertibility, causality, and more. We also distinguish the ARMA model from the ARMA process.

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Financial Time Series and Related Models, financial time series by real financial data. To characterize these facts, new models different from the Box-Jenkins ones are needed. And for this reason, ARCH models were firstly proposed by R. F. Engle in 1982 and have been extended by a great number of scholars since then. We also demonstrate ho

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Multivariate Time Series Analysis,ties with multivariate time series: identifiability and curse of dimensionality. Thus, this chapter focuses on a special and useful VAR models. First, basic concepts on multivariate time series and general VARMA models are introduced. Then, we elaborate on VAR model building, forecasting, Granger ca