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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Python Practice

Let's get into modeling. We'll start by giving some recommendations for users using MABs.


For this example, we'll take joke preferences by users, and we'll use them to simulate feedback on recommended jokes on our website. We'll use this feedback to tune our recommendations. We want to select the 10 best jokes to present to people visiting our site. The recommendations are going to be produced by 10 MABs that each have as many arms as there are jokes.

This is adapted from an example from the mab-ranking library on GitHub by Kenza-AI.

It's a handy library that comes with implementations of different bandits. I've simplified the installation of this library in my fork of the library, so we'll be using my fork here:

pip install git+

After this is finished, we can get right to it!

We'll download the jester dataset with joke preferences from...