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Machine Learning for Time-Series with Python

You're reading from  Machine Learning for Time-Series with Python

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Ben Auffarth Ben Auffarth
Profile icon Ben Auffarth

Table of Contents (15) Chapters

Preface 1. Introduction to Time-Series with Python 2. Time-Series Analysis with Python 3. Preprocessing Time-Series 4. Introduction to Machine Learning for Time-Series 5. Forecasting with Moving Averages and Autoregressive Models 6. Unsupervised Methods for Time-Series 7. Machine Learning Models for Time-Series 8. Online Learning for Time-Series 9. Probabilistic Models for Time-Series 10. Deep Learning for Time-Series 11. Reinforcement Learning for Time-Series 12. Multivariate Forecasting 13. Other Books You May Enjoy
14. Index

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...

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