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Build real-world time series forecasting systems which scale to
millions of time series by applying modern machine learning and
deep learning concepts Key Features Explore industry-tested machine
learning techniques used to forecast millions of time series Get
started with the revolutionary paradigm of global forecasting
models Get to grips with new concepts by applying them to
real-world datasets of energy forecasting Book DescriptionWe live
in a serendipitous era where the explosion in the quantum of data
collected and a renewed interest in data-driven techniques such as
machine learning (ML), has changed the landscape of analytics, and
with it, time series forecasting. This book, filled with
industry-tested tips and tricks, takes you beyond commonly used
classical statistical methods such as ARIMA and introduces to you
the latest techniques from the world of ML. This is a comprehensive
guide to analyzing, visualizing, and creating state-of-the-art
forecasting systems, complete with common topics such as ML and
deep learning (DL) as well as rarely touched-upon topics such as
global forecasting models, cross-validation strategies, and
forecast metrics. You'll begin by exploring the basics of data
handling, data visualization, and classical statistical methods
before moving on to ML and DL models for time series forecasting.
This book takes you on a hands-on journey in which you'll develop
state-of-the-art ML (linear regression to gradient-boosted trees)
and DL (feed-forward neural networks, LSTMs, and transformers)
models on a real-world dataset along with exploring practical
topics such as interpretability. By the end of this book, you'll be
able to build world-class time series forecasting systems and
tackle problems in the real world. What you will learn Find out how
to manipulate and visualize time series data like a pro Set strong
baselines with popular models such as ARIMA Discover how time
series forecasting can be cast as regression Engineer features for
machine learning models for forecasting Explore the exciting world
of ensembling and stacking models Get to grips with the global
forecasting paradigm Understand and apply state-of-the-art DL
models such as N-BEATS and Autoformer Explore multi-step
forecasting and cross-validation strategies Who this book is forThe
book is for data scientists, data analysts, machine learning
engineers, and Python developers who want to build industry-ready
time series models. Since the book explains most concepts from the
ground up, basic proficiency in Python is all you need. Prior
understanding of machine learning or forecasting will help speed up
your learning. For experienced machine learning and forecasting
practitioners, this book has a lot to offer in terms of advanced
techniques and traversing the latest research frontiers in time
series forecasting.
Ayyan Mani will not be constrained by Indian traditions. Despite
working at the Institute of Theory and Research in Mumbai as the
lowly personal assistant to a brilliant but insufferable
astronomer, he dreams of more for himself and his family. Ever wily
and ambitious, Ayyan weaves two plots: the first to cheer up his
weary, soap-opera-addicted wife by creating outrageous fictions
around their ten-year-old son; the other to sabotage the married
director by using his boss's seeming romance with the institute's
first female-and very attractive-researcher. Meanwhile, as the
institute's Brahmins wage a vicious war over theories about alien
life, Ayyan sees his deceptions intertwining and setting in motion
a series of extraordinary events he cannot stop. Unfailingly funny
and irreverent, Serious Men is at once a hilarious portrayal of
runaway egos and ambitions and a moving portrait of love and its
strange workings. One of 2010's "First Novels to Savor." -Sunday
Telegraph
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