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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

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Time Series Analysis with Python Cookbook

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Key benefits

  • Explore 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

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch. Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.

Who is this book for?

This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. 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 various imputation and interpolation strategies for missing data
  • Implement different models for univariate and multivariate time series
  • Use different deep learning libraries such as TensorFlow, Keras, and PyTorch
  • 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
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 30, 2022
Length: 630 pages
Edition : 1st
Language : English
ISBN-13 : 9781801075541
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Product Details

Publication date : Jun 30, 2022
Length: 630 pages
Edition : 1st
Language : English
ISBN-13 : 9781801075541
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Total $ 159.97
Time Series Analysis with Python Cookbook
$51.99
Modern Time Series Forecasting with Python
$52.99
Machine Learning with PyTorch and Scikit-Learn
$54.99
Total $ 159.97 Stars icon

Table of Contents

17 Chapters
Chapter 1: Getting Started with Time Series Analysis Chevron down icon Chevron up icon
Chapter 2: Reading Time Series Data from Files Chevron down icon Chevron up icon
Chapter 3: Reading Time Series Data from Databases Chevron down icon Chevron up icon
Chapter 4: Persisting Time Series Data to Files Chevron down icon Chevron up icon
Chapter 5: Persisting Time Series Data to Databases Chevron down icon Chevron up icon
Chapter 6: Working with Date and Time in Python Chevron down icon Chevron up icon
Chapter 7: Handling Missing Data Chevron down icon Chevron up icon
Chapter 8: Outlier Detection Using Statistical Methods Chevron down icon Chevron up icon
Chapter 9: Exploratory Data Analysis and Diagnosis Chevron down icon Chevron up icon
Chapter 10: Building Univariate Time Series Models Using Statistical Methods Chevron down icon Chevron up icon
Chapter 11: Additional Statistical Modeling Techniques for Time Series Chevron down icon Chevron up icon
Chapter 12: Forecasting Using Supervised Machine Learning Chevron down icon Chevron up icon
Chapter 13: Deep Learning for Time Series Forecasting Chevron down icon Chevron up icon
Chapter 14: Outlier Detection Using Unsupervised Machine Learning Chevron down icon Chevron up icon
Chapter 15: Advanced Techniques for Complex Time Series Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(11 Ratings)
5 star 81.8%
4 star 18.2%
3 star 0%
2 star 0%
1 star 0%
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Vandita Bothra Jul 27, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The author has done an amazing job with this book. When I use the terms comprehensive and hands on to describe this book, I 100% mean it. It does presume you have basic python knowledge but nonetheless provides you with a crash course link to brush up your skills. It has the code snippets required for smooth learning and links to dataset on GitHub to play around.As a Data analyst, I am familiar with stats/python/modeling to an extent but this book has really helped me to deliver on my project the past few months. In a way, this book is written to guide you through a project step by step. Starts with how to read the data, handle missing values, Outlier detection, EDA, multiple modeling techniques depending on your data and complexity. The mathematics are presented briefly and appropriately for each topics, not overloading your brain with information.And that is why I would recommend this book as it’s a widely used topics and this book can be used not only as a starting point but also as a reference point while working on a time series project.PS- It explains how to set up a virtual environment
Amazon Verified review Amazon
David Knickerbocker Oct 23, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I'm currently about halfway through the book, and up to this point, it's been mostly about getting data, cleaning it, dealing with missing data, and handling outliers. The fact that halfway through the book, they author is still discussing wrangling and outlier analysis is a good sign, to me. GIGO. Don't skip the fundamentals. The author goes slow, shows his work, and explains things very well.I'm finding the format and style of writing to be very clean and concise. The example code is very clean as well, which is not always the case in data science books.My favorite part so far has been the chapter on outlier analysis. With a background in security, that'll always be of interest to me.This is all very well explained. The title include the word "cookbook". I tend to have less use for cookbooks (and cheatsheets) as they usually cover things that can be found on StackOverflow. However, this book discusses approaches and feels more like a series of well-organized lessons on various important topics relating to time series analysis.I'm only about halfway through and am definitely going to keep going. I'll always have use for time series analysis, and there is always more to learn. This is a winner. I'm definitely enjoying this and have use for it in my work.
Amazon Verified review Amazon
Dr Abed M Hammoud Aug 23, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have read several good books on time series analysis; I find this book to have an excellent mix of theory and code. Very readable and full of ready-to-use code snippets. It is highly recommended.
Amazon Verified review Amazon
Moe Jul 02, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great easy to understand book for all of you Python fans, am still dipping my toes into the coding world and I find this book to be my cup of tea finding what works for me and what doesn’t. If you’re a bad reader like me and more of hands on person, then you need to look no further as this book is definitely for you, the book focuses on illustrating how and when to use codes clearly.
Amazon Verified review Amazon
Maribel Aug 12, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've read a few ML time-series books. One thing I look for is practicality as well as explaining difficult and advanced concepts. This book does a pretty good job for walking somebody through the process. I appreciate the visuals, the code, as well as all the technical depth in all the different models. I would definitely recommend this to an experienced practicioner to increase their skills.
Amazon Verified review Amazon
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