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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Unsupervised Methods for Time-Series

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In this chapter, we'll describe the specific challenges of unsupervised learning with time-series data. At the core of unsupervised learning is the extraction of structure from time-series, most importantly recognizing similarities between subsequences. This is the essence of anomaly detection (also: outlier detection), where we want to identify sequences that are notably different from the rest of the series.

Time-series data is usually non-stationary, non-linear, and dynamically evolving. An important challenge of working with time-series is recognizing the changes in the underlying processes. This is called change point detection (CPD...