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
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14
Index

Reinforcement Learning for Time-Series

Reinforcement Learning (RL) can and has been applied to time-series, however, the problem has to be framed in a certain way. For reinforcement learning, we need to have significant feedback between predictions and ongoing (actions) of the system.

In order to apply RL to time-series forecasting or predictions, the prediction has to condition an action, therefore the state evolution depends on the current state and the agent's action (and randomness). Hypothetically, rewards could be a performance metric about the accuracy of predictions. However, the consequences of good or bad predictions do not affect the original environment. Essentially this corresponds to a supervised learning problem.

More meaningfully, if we want to frame our situation as an RL problem, the state of the systems should be affected by the agents' decisions. For instance, in the case of interacting with the stock market, we would buy or sell based on predictions...