Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model.

Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models.

Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set.

Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. 

What You Will Learn

• Carry out forecasting with Python
• Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques
• Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing
• Select the right model for the right use case

Table of Contents

Part I: Machine Learning for Forecasting

Chapter 1: Models for Forecasting

Chapter 2: Model Evaluation for Forecasting

Part II: Univariate Time Series Models

Chapter 3: The AR Model

Chapter 4: The MA Model

Chapter 5: The ARMA Model

Chapter 6: The ARIMA Model

Chapter 7: The SARIMA Model

Part Ill: Multivariate Time Series Models

Chapter 8: The SARIMAX Model

Chapter 9: The VAR Model

Chapter 10: The VARMAX Model

Part IV: Supervised Machine Learning Models

Chapter 11: The Linear Regression

Chapter 12: The Decision Tree Model

Chapter 13: The kNN Model

Chapter 14: The Random Forest

Chapter 15: Gradient Boosting with XGBoost and LightGBM

Part V: Advanced Machine and Deep Learning Models

Chapter 16: Neural Networks

Chapter 17: RNNs Using SimpleRNN and GRU

Chapter 18: LSTM RNNs

Chapter 19: The Prophet Model

Chapter 20: The DeepAR Model

Chapter 21: Model Select ion

Who This Book Is For

The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.

About the Author

Joos is a data scientist, with over five years of industry experience in developing machine learning tools, of which a large part is forecasting models. He currently works at Disneyland Paris where he develops machine learning for a variety of tools. His experience in writing and teaching have motivated him to make this book on advanced forecasting with Python.