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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 data using auto_arima

For this recipe, you must install pmdarima, a Python library that includes auto_arima for automating ARIMA hyperparameter optimization and model fitting. The auto_arima implementation in Python is inspired by the popular auto.arima from the forecast package in R.

In Chapter 10, Building Univariate Time Series Models Using Statistical Methods, you learned that finding the proper orders for the AR and MA components is not simple. Although you explored useful techniques for estimating the orders, such as interpreting the partial autocorrelation function (PACF) and autocorrelation function (ACF) plots, you may still need to train different models to find the optimal configurations (referred to as hyperparameter tuning). This can be a time-consuming process and is where auto_arima shines.

Instead of the naive approach of training multiple models through grid search to cover every possible combination of parameter values, auto_arima automates...

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