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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A major determinant of data quality is drift. Drift (also: dataset shift) means that the patterns in data change over time. Drift is important because the performance of a machine learning model can be adversely affected by changes to the dataset.

Drift transitions can occur abruptly, incrementally, gradually, or be recurring. This is illustrated here:


Figure 8.4: Four types of concept drift transitions

When the transition is abrupt, it happens from one time step to another without apparent preparation or warning. In contrast, it can also be incremental in the sense that there's first a little shift, then a bigger shift, then a bigger shift again.

When a transition happens gradually, it can look like a back and forth between different forces until a new baseline is established. Yet another type of transition is recurring or cyclical when there's a regular or recurring shift between different baselines.

There are different kinds of drift: