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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

Detecting outliers using the Tukey method

This recipe will extend on the previous recipe, Detecting outliers using visualizations. In Figure 8.5, the box plot showed the quartiles with whiskers extending to the upper and lower fences. These boundaries or fences were calculated using the Tukey method.

Let's expand on Figure 8.5 with additional information of other components:

Figure 8.11: Box plot for the daily average taxi passengers data

Visualizations are great to give you a high-level perspective on the data you are dealing with, such as the overall distribution and potential outliers. Ultimately you want to identify these outliers programmatically so you can isolate these data points for further investigation and analysis. This recipe will teach you how to calculate IQR and define points that fall outside the lower and upper Tukey fences.

How to do it...

Most statistical methods allow you to spot extreme values beyond a certain threshold. For example, this could be the mean...

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