Tarek A. Atwan - Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
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Perform time series analysis and forecasting confidently with this Python code bank and reference manual
Key Features- 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
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, youll 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, youll work with ML and DL models using TensorFlow and PyTorch.
Finally, youll learn how to evaluate, compare, optimize models, and more using the recipes covered in the 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
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.
Table of Contents- Getting Started with Time Series Analysis
- Reading Time Series Data from Files
- Reading Time Series Data from Databases
- Persisting Time Series Data to Files
- Persisting Time Series Data to Databases
- Working with Date and Time in Python
- Handling Missing Data
- Outlier Detection Using Statistical Methods
- Exploratory Data Analysis and Diagnosis
- Building Univariate Time Series Models Using Statistical Methods
- Additional Statistical Modeling Techniques for Time Series
- Forecasting Using Supervised Machine Learning
- Deep Learning for Time Series Forecasting
- Outlier Detection Using Unsupervised Machine Learning
- Advanced Techniques for Complex Time Series
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Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
Tarek A. Atwan
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Reshma Raman
Senior Editors: Roshan Ravikumar, Tazeen Shaikh
Content Development Editor: Shreya Moharir
Technical Editor: Rahul Limbachiya
Copy Editor: Safis Editing
Project Coordinator: Aparna Nair
Proofreader: Safis Editing
Indexer: Manju Arasan
Production Designer: Vijay Kamble
Marketing Coordinator: Priyanka Mhatre
First published: June 2022
Production reference: 1100622
Published by Packt Publishing Ltd.
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ISBN 978-1-80107-554-1
www.packt.com
To my mother, who never gets tired of providing unconditional love and support; everything I am, and the person I will become, I owe it to you.
Tarek A. Atwan is a data analytics expert with over 16 years of international consulting experience, providing subject matter expertise in data science, machine learning operations, data engineering, and business intelligence. He has taught multiple hands-on coding boot camps, courses, and workshops on various topics, including data science, data visualization, Python programming, time series forecasting, and blockchain at different universities in the United States. He is regarded as an industry mentor and advisor, working with executive leaders in various industries to solve complex problems using a data-driven approach.
I owe a big thank you to Packt's fantastic editorial team for this project. Thank you for your dedication and persistence throughout this long journey to ensure this project is a success. I could not have done this without the love and support from my family, my mother, Hanan, Mohammad, Mai, and Ebtissam. I am grateful for the company of very talented and inspirational friends around me; thank you, Anish Sana and Ajit Sonawane, for being there. To my beautiful kids, this journey is for you to realize nothing in life comes easy; work hard, keep your heads up, and be the best you can be.
Emil Bogomolov is a machine learning (ML) lead at Youpi Inc. He is engaged in creating new ways of collaboration using video. Previously, he was a research engineer in the computer vision group at the Skolkovo Institute of Science and Technology. He is the co-author of papers published at international conferences, such as VISAPP, WACV, and CVPR, and an educational courses author on data analysis at online schools. Emil is also a frequent speaker at technology conferences and author of tech articles on ML and AI.
Jeevanshu Dua has been working in the data science industry since 2019. When he completed a small ML course, he was offered work as an assistant teacher of Python, as he has a degree in engineering. He has recently joined Rangam as a data scientist to start a new journey in his career. He loves teaching and talking about data science.
I had immense pleasure reading and being a part of this book.
Prajjwal Nijhara is an electrical engineering student at Aligarh Muslim University and a member of the AUV-ZHCET club, where he works on computer vision. He is a mentor at AMU-OSS and has worked with DeepSource as a developer-relation intern for 6 months.
Catherine Azam is the lead architect at GoBubble, where she helps make the internet a safer, kinder place for everybody with a social media page by developing emotional AI that shields users from hateful posts. She started her career as a statistician and has had exposure to industries as varied as financial services, telecommunications, automotive, and blockchain, including working for companies such as Sky and IBM. She has held various job titles, from researcher to data scientist, data engineer to cloud engineer, and enjoys exploring random subjects by looking at datasets anything that can be quantified, ranging from the hard sciences to humanities.
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