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Machine Learning for Time-Series with Python

You're reading from   Machine Learning for Time-Series with Python Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Length 370 pages
Edition 1st Edition
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Author (1):
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Ben Auffarth Ben Auffarth
Author Profile Icon Ben Auffarth
Ben Auffarth
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Time-Series with Python 2. Time-Series Analysis with Python FREE CHAPTER 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 Exercise

Let's put into practice what we've learned in this chapter so far. We'll be doing a model in Prophet, a Markov Switching model, a Fuzzy time-series model, and a BSTS model.

Let's get started with Prophet!

Prophet

First, let's make sure we have everything installed that we need. Let's quickly install the required libraries. We can do this from the terminal (or similarly from the Anaconda navigator):

pip install -U pandas-datareader plotly

You'll need a recent version of pandas-datareader, otherwise you might get a RemoteDataError.

We'll use the Prophet model through Facebook's Prophet library. Let's install it:

pip install prophet

Once this is done, we are set to go.

In this example, we'll use the daily Yahoo closing stock values in this chapter that we used in several examples in Chapter 7, Machine Learning Models for Time-Series.

To recap, we can download the daily Yahoo...

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