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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Python practice

Let's model airplane passengers. We'll forecast the monthly number of passengers.

This dataset is considered one of the classic time-series, published by George E.P. Box and Gwilym Jenkins alongside the book "Time-Series Analysis: Forecasting and Control" (1976). I have provided a copy of this dataset in the chapter10 folder of the book's GitHub repository. You can download it from there or use the URL directly in pd.read_csv().

We'll first start with a simple FCN and then we'll apply a recurrent network, and finally, we'll apply a very recent architecture, a Dilated Causal Convolutional Neural Network.

The FCN is first.

Fully connected network

In this first practice session, we'll use TensorFlow libraries, which we can quickly install from the terminal (or similarly from the anaconda navigator):

pip install -U tensorflow

We'll execute the commands from the Python (or IPython) terminal...