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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Time-Series Analysis with Python

Time-Series analysis revolves around getting familiar with a dataset and coming up with ideas and hypotheses. It can be thought of as "storytelling for data scientists" and is a critical step in machine learning, because it can inform and help shape tentative conclusions to test while training a machine learning model. Roughly speaking, the main difference between time-series analysis and machine learning is that time-series analysis does not include formal statistical modeling and inference.

While it can be daunting and seem complex, it is a generally very structured process. In this chapter, we will go through the fundamentals in Python for dealing with time-series patterns. In Python, we can do time-series analysis by interactively querying our data using a number of tools that we have at our fingertips. This starts from creating and loading time-series datasets to identifying trend and seasonality. We'll outline both the structure of time-series analysis, and the constituents both in terms of theory and practice in Python by going through examples.

The main example will use a dataset of air pollution in London and Delhi. You can find this example as a Jupyter notebook in the book's GitHub repository.

We're going to cover the following topics:

  • What is time-series analysis?
  • Working with time-series in Python
  • Understanding the variables
  • Uncovering relationships between variables
  • Identifying trend and seasonality

We'll start with a characterization and an attempt at a definition of time-series analysis.