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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Advanced Forecasting with Python - With State-of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR (Paperback, 1st ed.)
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Advanced Forecasting with Python - With State-of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR (Paperback, 1st ed.)
Expected to ship within 10 - 15 working days
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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.
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