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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 needed datasets from the GitHub repository:

Throughout the chapter, you will be using a dataset from the Numenta Anomaly Benchmark (NAB), which provides outlier detection benchmark datasets. For more information about NAB, please visit their GitHub repository here: https://github.com/numenta/NAB.

The New York Taxi dataset captures the number of NYC taxi passengers at a specific timestamp. The data contains known anomalies that are provided to evaluate the performance of our outlier detectors. The dataset contains 10,320 records between July 1, 2014, to May 31, 2015. The observations are captured in a 30-minute interval, which translates to freq = ‘...

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