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

Product type Paperback

Published in Jun 2022

Publisher Packt

ISBN-13 9781801075541

Length 630 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

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Table of Contents (18) Chapters

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

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Handling missing data with interpolation

Another commonly used technique for imputing missing values is interpolation. The pandas library provides the DataFrame.interpolate() method for more complex univariate imputation strategies.

For example, one of the interpolation methods available is linear interpolation. Linear interpolation can be used to impute missing data by drawing a straight line between the two points surrounding the missing value (in time series, this means for a missing data point, it looks at a prior past value and the next future value to draw a line between them). A polynomial interpolation, on the other hand, will attempt to draw a curved line between the two points. Hence, each method will have a different mathematical operation to determine how to fill in for the missing data.

The interpolation capabilities in pandas can be extended further through the SciPy library, which offers additional univariate and multivariate interpolations.

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

Table of Contents (18) Chapters

Handling missing data with interpolation

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

Table of Contents (18) Chapters

Handling missing data with interpolation

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