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
Other Books You May Enjoy
14
Index

Introduction to deep learning

Deep learning is based on fundamental concepts that find their roots early in the 20th century – the wiring between neurons. Neurons communicate chemically and electrically through so-called neurites.

This wiring was first described and drawn by Santiago Ramón y Cajal, a Spanish neuroscientist. He charted the anatomy of the brain and the structure of neural networks in the brain. He received the Nobel Prize in Physiology or Medicine in 1906, which he shared with Camillo Golgi, who invented the stains for neurons based on potassium dichromate and silver nitrate that Ramón y Cajal applied in his microscopy studies.

The chart below is just one of his elaborate drawings of the arborization of neural connections (called neurites – dendrites and axons) between neurons in the brain (source Wikimedia Commons):

ile:Debuixos Santiago Ramón y Cajal.jpg

Figure 10.1: Ramon y Cajal's drawing of networks of neurons in the brain

In the schematic, you can appreciate...