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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

What do you get with a Packt Subscription?

Free for first 7 days. ₹800 p/m after that. Cancel any time!

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Frequently bought together

Machine Learning with PyTorch and Scikit-Learn

₹4096.99

Time Series Analysis with Python Cookbook

₹3872.99

Modern Time Series Forecasting with Python

₹3947.99

Total ₹ 11,917.97

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Maribel Aug 12, 2022

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

Vandita Bothra Jul 27, 2022

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

David Knickerbocker Oct 23, 2022

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.

James Jul 01, 2022

This is a great reference book filled with many helpful code snippets. There are many insightful hints and tricks in the book, things I did not know even though I have used Python and Pandas for a while. I have read and owned many Time Series Analysis/Forecasting textbooks, mostly filled with R code, making it hard to transition or translate these concepts into Python. There are few Python Time Series books out there, these are great resources to understand the concepts within Time Series Analysis/Forecasting, but they lack proper code implementation (and explanations) to show how things work and the best way to implement them. I think this cookbook fills that gap between the different academic textbooks so you can feel comfortable in developing solutions that can be used in a real-world setting. It takes you from theory to hands-on implementation and many valuable hints to take things further.I liked the balance between ML, DL, and Statistical Methods, but also a fair amount of Data Engineering (how to read/write to Snowflake, AWS Redshift, InfluxDB, Cloud storage, and working with SAS files ..etc.). As a reference book, I find that it can be very beneficial for me in the future as it covers a wide range of topics, the entire end-to-end process, from data pipeline, Exploratory Data Analysis, Data Preparation for ML, and development models using ARIMA, SARIMA, VAR, UCM, Exponential Smoothing, Prophet, Machine Learning, and Deep Learning.The recipes do a great job comparing different methods and guide you to different evaluation techniques for univariate, multivariate, multiple-seasonality, and other types of time series data. One of the chapters does a great job comparing ML/DL, Prophet, with classical statistical methods, which I thought was pretty insightful.Overall highly recommended. It is a must-have book to be on your bookshelf whenever you get stuck or need help understanding or implementing solutions for Time Series Analysis.

Dr Abed M Hammoud Aug 23, 2022

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.

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: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

What do you get with a Packt Subscription?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with a Packt Subscription?

Product Details

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Recommendations for you

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