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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https://doi.org/10.1007/978-1-4939-6774-2ies presented in Chap. . and SARIMA modeling for seasonal time series to be considered in this chapter. Through case study, we demonstrate how to use Python to implement the Box-Jenkins method. In addition, we also discuss REGARMA models.

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EEG and Semiology in Generalized Epilepsies 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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Neonatal EEG and Neonatal Seizuresties 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

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Head Trauma and Posttraumatic Seizures,duce a few unit root and stationarity tests, as well as implement them with Python. We also elaborate on how to simulate a standard Brownian motion which is very useful in fields of finance and other disciplines. Finally, we concisely discuss Granger’s representation theorem and vector error correct

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978-3-031-13586-6The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerl

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