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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 Apr 2025
Publisher
ISBN-13 9781805124283
Length 98 pages
Edition 2nd Edition
Languages
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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 (13) Chapters Close

1. Time Series Analysis with Python Cookbook, Second Edition: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation FREE CHAPTER
2. Getting Started with Time Series Analysis 3. Reading Time Series Data from Files 4. Reading Time Series Data from Databases 5. Persisting Time Series Data to Files 6. Persisting Time Series Data to Databases 7. Working with Date and Time in Python 8. Handling Missing Data 9. Outlier Detection Using Statistical Methods 10. Exploratory Data Analysis and Diagnosis 11. Building Univariate Time Series Models Using Statistical Methods 12. Additional Statistical Modeling Techniques for Time Series 13. Outlier Detection Using Unsupervised Machine Learning

Technical requirements

You can download the Jupyter notebooks and the requisite datasets from the GitHub repository to follow along:

In this chapter and beyond, you will extensively use pandas 2.1.3 (released November 10, 2023). There will be four additional libraries that you will be using:

  • numpy (1.26.0)
  • matplotlob (3.8.1)
  • statsmodels (0.14.0)
  • scikit-learn (1.3.2)
  • SciPy (1.11.3)

If you are using pip, then you can install these packages from your terminal with the following command:

pip install matplotlib numpy statsmodels scikit-learn scipy

If you are using conda, then you can install these packages with the following command:

conda install matplotlib numpy statsmodels scikit-learn scipy

In this chapter, two datasets will be used extensively...

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