This book is intended for both veteran and new data analysts seeking an overview of modern time series analysis. Time series data and the analysis of such data are rapidly growing in new importance due to a number of massive producers of such data, such as the internet of things, the digitalization of health care, and the rise of smart cities. In the next decade (and beyond), the quantity and quality of time series data will grow rapidly.

As continuous monitoring and data collection grows increasingly common, the need for competent time series analysis with both traditional statistical methods and innovative machine learning techniques will continue to grow. This book will provide you with a background in a broad range of time series techniques useful for analyzing and predicting human behavior, scientific phenomena, and industrial pipelines.

Let’s start from scratch. What is time series analysis? Time series analysis is the endeavor of extracting meaningful summary and statistical information from points arranged in chronological order. This is done to diagnose past behavior as well as to predict future behavior. In this book we will use a variety of approaches, ranging from hundred-year-old statistical models to newly developed neural network architectures.

None of these techniques we will study has developed in a vacuum or out of purely theoretical interest. Innovations in time series analysis result from new ways of collecting, recording, and visualizing data. Those connections is what we will think about in this chapter.