Title Statements
- Full title :Applied Time Series Analysis and Forecasting with Python.
Edition Statements
- Publication Date:©2022.
Publication Statements
- Publisher(s):Springer International Publishing AG
- Place of Publication:Cham
- Language:eng
- Publication Date:©2022.
The Physical Object
- Description:1 online resource (377 pages).
Edition Identifiers
- ISBN:9783031135842, 3031135849
- oclc:on1351225215
- LC Classification:QA280
Table of Contents
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 Te…
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