data in your everyday work and select the right tools to analyze it.
What Does This Book Cover?
This book offers a comprehensive introduction to the core concepts, terminology, approaches, and applications of machine learning and deep learning for time series forecasting: understanding these principles leads to more flexible and successful time series applications.
In particular, the following chapters are included:
Chapter 1: Overview of Time Series Forecasting This first chapter of the book is dedicated to the conceptual introduction of time series, where you can learn the essential aspects of time series representations, modeling, and forecasting, such as time series analysis and supervised learning for time series forecasting.We will also look at different Python libraries for time series data and how libraries such as pandas, statsmodels, and scikit-learn can help you with data handling, time series modeling, and machine learning, respectively.Finally, I will provide you with general advice for setting up your Python environment for time series forecasting.
Chapter 2: How to Design an End-to-End Time Series Forecasting Solution on the Cloud The purpose of this second chapter is to provide an end-to-end systematic guide for time series forecasting from a practical and business perspective by introducing a time series forecasting template and a real-world data science scenario that we use throughout this book to showcase some of the time series concepts, steps, and techniques discussed.
Chapter 3: Time Series Data Preparation In this chapter, I walk you through the most important steps to prepare your time series data for forecasting models. Good time series data preparation produces clean and well-curated data, which leads to more practical, accurate predictions.Python is a very powerful programming language to handle data, offering an assorted suite of libraries for time series data and excellent support for time series analysis, such as SciPy, NumPy, Matplotlib, pandas, statsmodels, and scikit-learn.You will also learn how to perform feature engineering on time series data, with two goals in mind: preparing the proper input data set that is compatible with the machine learning algorithm requirements and improving the performance of machine learning models.
Chapter 4: Introduction to Autoregressive and Automated Methods for Time Series Forecasting In this chapter, you discover a suite of autoregressive methods for time series forecasting that you can test on your forecasting problems. The different sections in this chapter are structured to give you just enough information on each method to get started with a working code example and to show you where to look to get more information on the method.We also look at automated machine learning for time series forecasting and how this method can help you with model selection and hyperparameter tuning tasks.
Chapter 5: Introduction to Neural Networks for Time Series Forecasting In this chapter, I discuss some of the practical reasons data scientists may still want to think about deep learning when they build time series forecasting solutions. I then introduce recurrent neural networks and show how you can implement a few types of recurrent neural networks on your time series forecasting problems.
Chapter 6: Model Deployment for Time Series Forecasting In this final chapter, I introduce Azure Machine Learning SDK for Python to build and run machine learning workflows. You will get an overview of some of the most important classes in the SDK and how you can use them to build, train, and deploy a machine learning model on Azure.Through machine learning model deployment, companies can begin to take full advantage of the predictive and intelligent models they build and, therefore, transform themselves into actual AI-driven businesses.Finally, I show how to build an end-to-end data pipeline architecture on Azure and provide deployment code that can be generalized for different time series forecasting solutions.
Reader Support for This Book
This book also features extensive sample code and tutorials using Python, along with its technical libraries, that readers can leverage to learn how to solve real-world time series problems.
Readers can access the sample code and notebooks at the following link: aka.ms/ML4TSFwithPython
Companion Download Files
As you work through the examples in this book, the project files you need are all available for download from aka.ms/ML4TSFwithPython.
Each file contains sample notebooks and data that you can use to validate your knowledge, practice your technical skills, and build your own time series forecasting solutions.
How to Contact the Publisher
If you believe you've found a mistake in this book, please bring it to our attention. At John Wiley & Sons, we understand how important it is to provide our customers with accurate content, but even with our best efforts an error may occur.
In order to submit your possible errata, please email it to our customer service team at [email protected] with the subject line “Possible Book Errata Submission.”
How to Contact the Author
We appreciate your input and questions about this book! You can find me on Twitter at @frlazzeri.
CHAPTER 1 Overview of Time Series Forecasting
Time series is a type of data that measures how things change over time. In a time series data set, the time column does not represent a variable per se: it is actually a primary structure that you can use to order your data set. This primary temporal structure makes time series problems more challenging as data scientists need to apply specific data preprocessing and feature engineering techniques to handle time series data.
However, it also represents a source of additional knowledge that data scientists can use to their advantage: you will learn how to leverage this temporal information to extrapolate insights from your time series data, like trends and seasonality information, to make your time series easier to model and to use it for future strategy and planning operations in several industries. From finance to manufacturing and health care, time series forecasting has always played a major role in unlocking business insights with respect to time.
Following are some examples of problems that time series forecasting can help you solve:
What are the expected sales volumes of thousands of food groups in different grocery stores next quarter?
What are the resale values of vehicles after leasing them out for three years?
What are passenger numbers for each major international airline route and for each class of passenger?
What is the future electricity load in an energy supply chain infrastructure, so that suppliers can ensure efficiency and prevent energy waste and theft?
The
