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Machine Learning for Time Series Forecasting with Python®
Francesca Lazzeri, PhD
Introduction
Time series data is an important source of information used for future decision making, strategy, and planning operations in different industries: from marketing and finance to education, healthcare, and robotics. In the past few decades, machine learning model-based forecasting has also become a very popular tool in the private and public sectors.
Currently, most of the resources and tutorials for machine learning model-based time series forecasting generally fall into two categories: code demonstration repo for certain specific forecasting scenarios, without conceptual details, and academic-style explanations of the theory behind forecasting and mathematical formula. Both of these approaches are very helpful for learning purposes, and I highly recommend using those resources if you are interested in understanding the math behind theoretical hypotheses.
This book fills that gap: in order to solve real business problems, it is essential to have a systematic and well-structured forecasting framework that data scientists can use as a guideline and apply to real-world data science scenarios. The purpose of this hands-on book is to walk you through the core steps of a practical model development framework for building, training, evaluating, and deploying your time series forecasting models.
The first part of the book (Chapters 1 and 2) is dedicated to the conceptual introduction of time series, where you can learn the essential aspects of time series representations, modeling, and forecasting.
In the second part (Chapters 3 through 6), we dive into autoregressive and automated methods for forecasting time series data, such as moving average, autoregressive integrated moving average, and automated machine learning for time series data. I then introduce neural networks for time series forecasting, focusing on concepts such as recurrent neural networks (RNNs) and the comparison of different RNN units. Finally, I guide you through the most important steps of model deployment and operationalization on Azure.
Along the way, I show at practice how these models can be applied to real-world data science scenarios by providing examples and using a variety of open-source Python packages and Azure. With these guidelines in mind, you should be ready to deal
