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 do first an example of anomaly detection, then another for CPD. Let's first look at the needed libraries in the next section.


In this chapter, we'll use several libraries, which we can quickly install from the terminal (or similarly from the anaconda navigator):

pip install ruptures alibi_detect

We'll execute the commands from the Python (or IPython) terminal, but equally we could execute them from a Jupyter notebook (or a different environment).

We should be ready now to get into the woods with implementing unsupervised time-series algorithms in Python.

Anomaly detection

alibi-detect comes with several benchmark datasets for time-series anomaly detection:

  • fetch_ecg—ECG dataset from the BIDMC Congestive Heart Failure Database
  • fetch_nab—Numenta Anomaly Benchmark
  • fetch_kdd—KDD Cup '99 dataset of computer network intrusions

The last of these is...