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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In this chapter, we've discussed how probabilistic models for time-series can help us make decisions with an estimate of uncertainty in the context of financial forecasting. These forecasts drive business decisions for financial planning.

I've introduced Prophet, Markov models, and Fuzzy time-series models. We've discussed the components of Facebook's Prophet model. For Markov models, we've discussed the main ideas, such as the Markov property, and we've discussed more details regarding Switching Models. Then I've explained some basics of fuzzy set theory and how this is applied to time-series.

Finally, we've delved into the intuition and some of the theory of BSTS models in the context of estimating treatment effects in experiments.

Finally, we went through an applied exercise with each method. In the BSTS practice, we've looked at the effect of the Volkswagen emissions scandal.