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Time Series Analysis with Python Cookbook

You're reading from   Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801075541
Length 630 pages
Edition 1st Edition
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Author (1):
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Tarek A. Atwan Tarek A. Atwan
Author Profile Icon Tarek A. Atwan
Tarek A. Atwan
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Time Series Analysis 2. Chapter 2: Reading Time Series Data from Files FREE CHAPTER 3. Chapter 3: Reading Time Series Data from Databases 4. Chapter 4: Persisting Time Series Data to Files 5. Chapter 5: Persisting Time Series Data to Databases 6. Chapter 6: Working with Date and Time in Python 7. Chapter 7: Handling Missing Data 8. Chapter 8: Outlier Detection Using Statistical Methods 9. Chapter 9: Exploratory Data Analysis and Diagnosis 10. Chapter 10: Building Univariate Time Series Models Using Statistical Methods 11. Chapter 11: Additional Statistical Modeling Techniques for Time Series 12. Chapter 12: Forecasting Using Supervised Machine Learning 13. Chapter 13: Deep Learning for Time Series Forecasting 14. Chapter 14: Outlier Detection Using Unsupervised Machine Learning 15. Chapter 15: Advanced Techniques for Complex Time Series 16. Index 17. Other Books You May Enjoy

Forecasting time series with multiple seasonal patterns using Prophet

You were introduced to Prophet in Chapter 11, Additional Statistical Modeling Techniques for Time Series, under the Forecasting time series data using Facebook Prophet recipe, using the milk production dataset. The milk production dataset is a monthly dataset with a trend and one seasonal pattern.

The goal of this recipe is to show how you can use Prophet to solve a more complex dataset with multiple seasonal patterns. Preparing time series data for supervised learning is an important step, as shown in Chapter 12, Forecasting Using Supervised Machine Learning, and Chapter 13, Deep Learning for Time Series Forecasting. Similarly, the feature engineering technique for creating additional features is an important topic when training and improving machine learning and deep learning models. One benefit of using algorithms such as the UCM (introduced in the previous recipe) or Prophet is that they require little to...

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