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       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.