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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Online learning for time-series

There are two main scenarios of learning – online learning and offline learning. Online learning means that you are fitting your model incrementally as the data flows in (streaming data). On the other hand, offline learning, the more commonly known approach, implies that you have a static dataset that you know from the start, and the parameters of your machine learning algorithm are adjusted to the whole dataset at once (often loading the whole dataset into memory or in batches).

There are three major use cases for online learning:

  • Big data
  • Time constraints (for example, real time)
  • Dynamic environments

Typically, in online learning settings, you have more data, and it is appropriate for big data. Online learning can be applied to large datasets, where it would be computationally infeasible to train over the entire dataset.

Another use case for online learning is where the inference and fitting are performed...