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
Other Books You May Enjoy
14
Index

Bayesian Structural Time-Series Models

In causal inference, we want to analyze the effect of a treatment. The treatment can be any action that interacts with the system or environment that we care about, from changing the colors of a button on a website to the release of a product. We have the choice of taking the action (for example, releasing the product), thereby observing the outcome under treatment, or not taking the action, where we observe the outcome under no treatment. This is illustrated in the diagram here:

../causal%20(1).png

Figure 9.3: Causal effect of a treatment

In the diagram, an action is taken or not (medicine is administered to a patient), and depending on whether the action is taken we see the patient recovering (cycling) or going into intensive care.

A causal effect is the difference between what happens under treatment and what happens under no treatment. The problem with this is that we can't observe both potential outcomes at the same time.

However,...