Time Series Algorithms Recipes
Implement Machine Learning and Deep Learning Techniques with Python
This Apress imprint is published by the registered company APress Media, LLC, part of Springer Nature.
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To our families
Before reading this book, you should have a basic knowledge of statistics, machine learning, and Python programming. If you want to learn how to build basic to advanced time series forecasting models, then this book will help by providing recipes for implementation in Python. By the end of the book, you will have practical knowledge of all the different types of modeling methods in time series.
The desire to know the unknown and to predict the future has been part of human culture for ages. This desire has driven mankind toward the discipline of forecasting. Time series forecasting predicts unknown future data points based on the data's previous (past) observed pattern. It can depend not only on the previous target points and time (univariate) but also on other independent variables (multivariate). This book is a cookbook containing various recipes to handle time series forecasting.
Data scientists starting a new time series project but don’t have prior experience in this domain can easily utilize the various recipes in this book, which are domain agnostic, to kick-start and ease their development process.
This book is divided into five chapters. Chapter 1 covers recipes for reading and processing the time series data and basic Exploratory Data Analysis (EDA). The following three chapters cover various forecasting modeling techniques for univariate and multivariate datasets. Chapter 2 has recipes for multiple statistical univariate forecasting methods, with more advanced techniques continued in Chapter 3. Chapter 3 also covers statistical multivariate methods. Chapter 4 covers time series forecasting using machine learning (regression-based). Chapter 5 is on advanced time series modeling methods using deep learning.
The code for all the implementations in each chapter and the required datasets is available for download at github.com/apress/time-series-algorithm-recipes.
is an artificial intelligence (AI) and machine learning (ML) evangelist and thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science–led strategic transformations. He is a Google developer, an author, and a regular speaker at major AI and data science conferences (including the O’Reilly Strata Data & AI Conference and Great Indian Developer Summit (GIDS)). He is a visiting faculty member at some of the top graduate institutes in India. In 2019, he was featured as one of India’s “top 40 under 40” data scientists. In his spare time, Akshay enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.
is a data science and MLOps leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside organizations to solve problems through training programs. He always wants to stay ahead of the curve. Adarsha has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.
is a senior AI consultant. He has worked with global clients across multiple domains to help them solve their business problems using machine learning, natural language processing (NLP), and deep learning. Anoosh is passionate about guiding and mentoring people in their data science journey. He leads data science/machine learning meet-ups and helps aspiring data scientists navigate their careers. He also conducts ML/AI workshops at universities and is actively involved in conducting webinars, talks, and sessions on AI and data science. He lives in Bangalore with his family.
is a data scientist and MLOps engineer. He has worked with various global clients across multiple domains and helped them to solve their business problems extensively using advanced ML applications. He has experience across multiple fields of AI-ML, including time series forecasting, deep learning, NLP, ML operations, image processing, and data analytics. Presently, he is working on a state-of-the-art value observability suite for models in production, which includes continuous model and data monitoring along with the business value realized. He presented a paper, “Deep Learning Based Approach for Range Estimation,” written in collaboration with the DRDO, at an IEEE conference. He lives in Chennai with his family.
is a co-founder of DOCONVID AI. He is a computer science and engineering graduate with a decade of experience building solutions and platforms on applied machine learning. He has worked with NTT DATA, PwC, and Thoucentric and is now working on applied AI research in medical imaging and decentralized privacy-preserving machine learning in healthcare. Krishnendu is an alumnus of the MIT Entrepreneurship and Innovation Bootcamp and devotes his free time as an applied AI and ML research volunteer for various research NGOs and universities across the world.