| Description |
1 online resource (x, 372 pages) : illustrations (chiefly color) |
| Series |
Statistics and computing |
|
Statistics and computing.
|
| 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 |
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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 |
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3.1.3 KPSS Stationarity Test -- 3.2 Moving Average Models -- 3.2.1 Definition of Moving Average Models -- 3.2.2 Properties of MA Models -- 3.2.3 Invertibility -- 3.3 Autoregressive Models -- 3.3.1 Definition of Autoregressive Models -- 3.3.2 Durbin-Levinson Recursion Algorithm -- 3.3.3 Properties of Autoregressive Models -- 3.3.4 Stationarity and Causality of AR Models -- 3.4 Autoregressive Moving Average Models -- 3.4.1 Definitions -- 3.4.2 Properties of ARMA Models -- Problems -- 4 ARMA and ARIMA Modeling and Forecasting -- 4.1 Model Building Problems -- 4.2 Estimation Methods |
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4.2.1 The Innovations Algorithm -- 4.2.2 Method of Moments -- 4.2.3 Method of Conditional Least Squares -- 4.2.4 Method of Maximum Likelihood -- 4.3 Order Determination -- 4.4 Diagnosis of Models -- 4.5 Forecasting -- 4.6 Examples -- Problems -- 5 Nonstationary Time Series Models -- 5.1 The Box-Jenkins Method -- 5.1.1 Seasonal Differencing -- 5.1.2 SARIMA Models -- 5.2 SARIMA Model Building -- 5.2.1 General Idea -- 5.2.2 Case Studies -- 5.3 REGARMA Models -- Problems -- 6 Financial Time Series and Related Models -- 6.1 Stylized Facts of Financial Time Series -- 6.1.1 Examples of Return Series |
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6.1.2 Stylized Facts of Financial Time Series -- 6.2 GARCH Models -- 6.2.1 ARCH Models -- 6.2.2 GARCH Models -- 6.2.3 Estimation and Testing -- 6.2.4 Examples -- 6.3 Other Extensions -- 6.3.1 EGARCH Models -- 6.3.2 TGARCH Models -- 6.3.3 An Example -- Problems -- 7 Multivariate Time Series Analysis -- 7.1 Basic Concepts -- 7.1.1 Covariance and Correlation Matrix Functions -- 7.1.2 Stationarity and Vector White Noise -- 7.1.3 Sample Covariance and Correlation Matrices -- 7.1.4 Multivariate Portmanteau Test -- 7.2 VARMA Models -- 7.2.1 Definitions -- 7.2.2 Properties |
| 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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed October 28, 2022) |
| Subject |
Time-series analysis.
|
|
Time-series analysis -- Forecasting
|
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Time-series analysis -- Computer programs.
|
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Python (Computer program language)
|
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Python (Lenguaje de programación)
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Análisis de series temporales
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Análisis de series temporales -- Previsión
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Análisis de series temporales -- Programas de ordenador
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Python (Computer program language)
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Time-series analysis
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Time-series analysis -- Computer programs
|
| Form |
Electronic book
|
| Author |
Petukhina, Alla, author
|
| ISBN |
9783031135842 |
|
3031135849 |
|
