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
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.
