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Time Series Analysis with Python Cookbook
Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation , Second Edition

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Profile Icon Tarek A. Atwan
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Early Access
€18.99 per month
Paperback Apr 2025 98 pages 2nd Edition
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Renews at €18.99p/m
Arrow left icon
Profile Icon Tarek A. Atwan
Arrow right icon
Early Access
€18.99 per month
Paperback Apr 2025 98 pages 2nd Edition
Subscription
Free Trial
Renews at €18.99p/m
Subscription
Free Trial
Renews at €18.99p/m

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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook, Second Edition: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Welcome to Packt Early Access. We’re giving you an exclusive preview of this book before it goes on sale. It can take many months to write a book, but our authors have cutting-edge information to share with you today. Early Access gives you an insight into the latest developments by making chapter drafts available. The chapters may be a little rough around the edges right now, but our authors will update them over time.

You can dip in and out of this book or follow along from start to finish; Early Access is designed to be flexible. We hope you enjoy getting to know more about the process of writing a Packt book.

  1. Chapter 1: Getting Started with Time Series Analysis
  2. Chapter 2: Reading Time Series Data from Files
  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 14: Outlier Detection Using Unsupervised Machine Learning
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Key benefits

  • Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms
  • Learn different techniques for evaluating, diagnosing, and optimizing your models
  • Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities

Description

To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples. You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods. Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python.

Who is this book for?

This book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is a prerequisite. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

What you will learn

  • Understand what makes time series data different from other data
  • Apply imputation and interpolation strategies to handle missing data
  • Implement an array of models for univariate and multivariate time series
  • Plot interactive time series visualizations using hvPlot
  • Explore state-space models and the unobserved components model (UCM)
  • Detect anomalies using statistical and machine learning methods
  • Forecast complex time series with multiple seasonal patterns
  • Use conformal prediction for constructing prediction intervals for time series

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 30, 2025
Length: 98 pages
Edition : 2nd
Language : English
ISBN-13 : 9781805124283
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Languages :
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What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
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This Early Access product may have unedited chapters and, although we aim for accuracy, content may be updated during development
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
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Product Details

Publication date : Apr 30, 2025
Length: 98 pages
Edition : 2nd
Language : English
ISBN-13 : 9781805124283
Category :
Languages :
Concepts :

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Table of Contents

13 Chapters
Time Series Analysis with Python Cookbook, Second Edition: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation Chevron down icon Chevron up icon
Getting Started with Time Series Analysis Chevron down icon Chevron up icon
Reading Time Series Data from Files Chevron down icon Chevron up icon
Reading Time Series Data from Databases Chevron down icon Chevron up icon
Persisting Time Series Data to Files Chevron down icon Chevron up icon
Persisting Time Series Data to Databases Chevron down icon Chevron up icon
Working with Date and Time in Python Chevron down icon Chevron up icon
Handling Missing Data Chevron down icon Chevron up icon
Outlier Detection Using Statistical Methods Chevron down icon Chevron up icon
Exploratory Data Analysis and Diagnosis Chevron down icon Chevron up icon
Building Univariate Time Series Models Using Statistical Methods Chevron down icon Chevron up icon
Additional Statistical Modeling Techniques for Time Series Chevron down icon Chevron up icon
Outlier Detection Using Unsupervised Machine Learning Chevron down icon Chevron up icon
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