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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 Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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

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Amazon verified reviews

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

Amazon Verified review

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.

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.

Moe Jul 02, 2022

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.

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.

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

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