Aileen Nielsen - Practical Time Series Analysis: Prediction with Statistics and Machine Learning

by Aileen Nielsen

Copyright 2020 Aileen Nielsen. All rights reserved.

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• October 2019: First Edition

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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.