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)
13
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14
Index

Python exercise

Let's put into practice what we've learned in this chapter so far.

As for requirements, in this chapter, we'll be installing requirements for each section separately. The installation can be performed from the terminal, the notebook, or from the anaconda navigator.

In a few of the following sections, we'll demonstrate classification in a forecast, so some of these approaches will not be comparable. The reader is invited to do forecasts and classification using each approach and then compare results.

As a note of caution, both Kats and Greykite (at the time of writing) are very new libraries, so there might still be frequent changes to dependencies. They might pin your NumPy version or other commonly used libraries. Therefore, I'd recommend you install them in virtual environments separately for each section.

We'll go through this setup in the next section.

Virtual environments

In a Python virtual environment...