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)
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In this chapter, we have concentrated on two aspects of unsupervised methods for time-series:

  • Anomaly detection
  • Change point detection

The essence of anomaly detection (also: outlier detection) is to identify sequences that are notably different from the rest of the series. We've investigated different anomaly detection methods, and how several major companies are dealing with it at scale.

When working with time-series, it's important to be aware of changes in the data over time that makes models useless (model staleness). This is called change point detection and drift detection.

We've looked at change point detection in this chapter. In Chapter 8, Online Learning for Time-Series, we'll look at drift detection in more detail.