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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More machine learning methods for time-series

The algorithms that we'll cover in this section are all highly competitive for forecasting and prediction tasks. If you are looking for a discussion of state-of-the-art machine learning algorithms, please refer to Chapter 4, Introduction to Machine Learning for Time-Series.

In the aforementioned chapter, we've briefly discussed a few of these algorithms, but we'll discuss them here in more detail and we will also introduce other algorithms that we haven't discussed before, such as Silverkite, gradient boosting, and k-nearest neighbors.

We'll dedicate a separate practice section to a library that was released in 2021, which is facebook's Kats. Kats provides many advanced features, including hyperparameter tuning and ensemble learning. On top of these features, they implement feature extraction based on the TSFresh library and include many models, including Prophet, SARIMA, and others. They...