Nielsen - Practical time series analysis: prediction with statistics and machine learning
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Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.
Youll get the guidance you need to confidently:
Find and wrangle time series data
Undertake exploratory time series data analysis
Store temporal data
Simulate time series data
Generate and select features for a time series
Measure error
Forecast and classify time series with machine or deep learning
Evaluate accuracy and performance
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by Aileen Nielsen
Copyright 2020 Aileen Nielsen. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- October 2019: First Edition
- 2019-09-20: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781492041658 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Practical Time Series Analysis, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-492-04165-8
[MBP]
Weather, stock markets, and heartbeats. They all form time series. If youre interested in diverse data and forecasting the future, youre interested in time series analysis.
Welcome to Practical Time Series Analysis! If you picked this book up, youve probably already noticed that time series data is everywhere. Time series data grows increasingly ubiquitous and important as the big data ecosystem expands. For better or worse, sensors and tracking mechanisms are everywhere, and as a result there are unprecedented amounts of high-quality time series data available. Time series are uniquely interesting because they can address questions of causality, trends, and the likelihood of future outcomes. This book will take you through the major techniques that are most commonly applied to time series to address such questions.
Time series data spans a wide range of disciplines and use cases. It can be anything from customer purchase histories to conductance measurements of a nano-electronic system to digital recordings of human language. One point we discuss throughout the book is that time series analysis applies to a surprisingly diverse set of data. Any data that has an ordered axis can be analyzed with time series methods, even if that ordered axis is not time per se. Traditional time series data, such as stock data and weather patterns, can be analyzed with time series methods, but so can quirky data sets such as spectrographs of wine, where the time axis is actually an axis of frequency. Time series are everywhere.
There are two kinds of intended readers for this book. The first and larger category of reader is that of a data scientist who has rarely worked with time series data. This person could be an industry veteran or a junior analyst. The more experienced data analyst can skim the introductory conceptual areas of each chapter but will still benefit from this books discussions about best practices as well as pitfalls of working with time series data. A newer data analyst might consider working through the book in its entirety, although I have tried to keep each topic as self-contained as possible.
The second category of reader is someone supervising analytics at an organization with an extensive in-house data collection. If you are a member of this group, you will still need some technical background, but its not necessary that you be currently coding in your professional life. For such a reader, this book is useful to point out opportunities for your organization to use time series analysis even if it is not currently practiced in-house. This book will point you to new kinds of questions and analyses your organization can address with your existing data resources.
With respect to coding, you should have some familiarity with R and Python, especially with certain fundamental packages (in Python: NumPy, Pandas, and scikit-learn; and in R: data.table). The code samples should be readable even without all the background, but in that case you may need to take a short detour to familiarize yourself with these packages. This is most likely the case with respect to Rs data.table, an underused but highly performant data frame package that has fantastic time functionality .
In all cases, I have provided brief overviews of the related packages, some example code, and descriptions of what the code does. I also point the reader toward more complete overviews of the most used packages.
With respect to statistics and machine learning, you should have some familiarity with:
Introductory statisticsIdeas such as variance, correlation, and probability distributions
Machine learningClustering and decision trees
Neural networksWhat they are and how they are trained
For these cases, I provide a brief overview of such concepts within the text, but the uninitiated should read more deeply about them before continuing with some chapters. For most topics, I provide links to recommended free online resources for brief tutorials on the fundamentals of a given topic or technique.
I wrote this book for three reasons.
First, time series is an important aspect of data analysis but one that is not found in the standard data science toolkit. This is unfortunate both because time series data is increasingly available and also because it answers questions that cross-sectional data cannot. An analyst who does not know fundamental time series analysis is not making the most of their data. I hoped that this book could fill an existing and important void.
Second, when I started writing this book, I knew of no centralized overview of the most important aspects of time series analysis from a modern data science perspective. There are many excellent resources available for traditional time series analysis, most notably in the form of classic textbooks on statistical time series analysis. There are also many excellent individual blog posts on both traditional statistical methods and on machine learning or neural network approaches to time series. However, I could not identify a single centralized resource to outline all these topics and relate them to one another. The goal of this book is to provide that resource: a broad, modern, and practical overview of time series analysis covering the full pipeline for time series data and modeling. Again, I hoped that this book could fill an existing and important void.
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