Applied time series analysis and forecasting with Python

by Huang, Changquan

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Title Statements

Full title :Applied time series analysis and forecasting with Python / Changquan Huang, Alla Petukhina.

Summary

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

Edition Statements

Publication Date:©2022

Publication Statements

Publisher(s):Springer
Place of Publication:Cham
Language:eng
Publication Date:©2022

The Physical Object

Description:1 online resource (x, 372 pages) : : illustrations (chiefly color).

Edition Identifiers

ISBN:9783031135842, 3031135849
DOI:10.1007/978-3-031-13584-2
oclc:on1348379728
LC Classification:QA280
Dewey Decimal:519.5/5

Intro -- Preface -- Contents -- 1 Time Series Concepts and Python -- 1.1 The Concept of Time Series -- 1.1.1 What Is Time Series -- 1.1.2 Brief History of Time Series Analysis -- 1.1.3 Objectives of Time Series Analysis -- 1.2 The Programming Language Python -- 1.2.1 Introduction and Installing -- 1.2.2 Demonstrations -- 1.2.3 Python Extension Packages and Some Usages -- 1.3 Time Series Moment Functions and Stationarity -- 1.3.1 Moment Functions -- 1.3.2 Stationarity and Ergodicity -- 1.3.3 Sample Autocorrelation Function -- 1.3.4 White Noise and Random Walk

1.4 Time Series Data Visualization -- Problems -- 2 Exploratory Time Series Data Analysis -- 2.1 Partial Autocorrelation Functions -- 2.1.1 Definition of PACF -- 2.1.2 Sample PACF and PACF Plot -- 2.2 White Noise Test -- 2.3 Simple Time Series Compositions -- 2.4 Time Series Decomposition and Smoothing -- 2.4.1 Deterministic Components and Decomposition Models -- 2.4.2 Decomposition and Smoothing Methods -- 2.4.3 Example -- Problems -- 3 Stationary Time Series Models -- 3.1 Backshift Operator, Differencing, and Stationarity Test -- 3.1.1 Backshift Operator -- 3.1.2 Differencing and Stationarity

3.1.3 KPSS Stationarity Test -…