Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801075541
Length 630 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Tarek A. Atwan Tarek A. Atwan
Author Profile Icon Tarek A. Atwan
Tarek A. Atwan
Arrow right icon
View More author details
Toc

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

Technical requirements

You will be working with the sktime library, described as "a unified framework for machine learning with time series". Behind the scenes, sktime is a wrapper to other popular ML and time series libraries, including scikit-learn. It is recommended to create a new virtual environment for Python so that you can install all the required dependencies without any conflicts or issues with your current environment.

If you need a quick refresher on creating a virtual Python environment, check out the Development environment setup recipe, from Chapter 1, Getting Started with Time Series Analysis. The chapter covers two methods: using conda and venv.

The following instructions will show how to create a virtual environment using conda. You can call the environment any name you like. For the following example, we will name the environment sktime:

>> conda create -n sktime python=3.9 -y
>> conda activate sktime
>> conda install -c conda...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime