Book Image

Machine Learning for Time-Series with Python

By : Ben Auffarth
Book Image

Machine Learning for Time-Series with Python

By: Ben Auffarth

Overview of this book

The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.
Table of Contents (15 chapters)
Other Books You May Enjoy

Machine learning algorithms for time-series

An important distinction in machine learning for time-series is the one between univariate and multivariate, in which algorithms are univariate, which means that they can only work with a single feature, or multi-variate, which means that they work with many features.

In univariate datasets, each case has a single series and a class label. Earlier models (classical modeling) focused on univariate datasets and applications. This is also reflected in the availability of datasets.

One of the most important repositories for time-series datasets, the UCR (University of California, Riverside) archive, which was released first in 2002, has provided a valuable resource for univariate time-series. It now contains about 120 datasets, but is lacking multivariate datasets. Furthermore, the M competitions (especially M3, 4, and 5) have a lot of available time-series datasets.

Multivariate time-series are datasets that have multiple feature...