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    MODERN TIME SERIES FORECASTING WITH PYTHON

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    Sinopse

    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 Description
    We 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 for
    The 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.

    Table of Contents
    Introducing Time Series
    Acquiring and Processing Time Series Data
    Analyzing and Visualizing Time Series Data
    Setting a Strong Baseline Forecast
    Time Series Forecasting as Regression
    Feature Engineering for Time Series Forecasting
    Target Transformations for Time Series Forecasting
    Forecasting Time Series with Machine Learning Models
    Ensembling and Stacking
    Global Forecasting Models
    Introduction to Deep Learning
    Building Blocks of Deep Learning for Time Series
    Common Modeling Patterns for Time Series
    Attention and Transformers for Time Series
    Strategies for Global Deep Learning Forecasting Models
    (N.B. Please use the Look Inside option to see further chapters)

    Ficha Técnica

    Especificações

    ISBN9781803246802
    SubtítuloEXPLORE INDUSTRY-READY TIME SERIES FORECASTING USING MODERN MACHINE LEARNING AND DEEP LEARNING
    Pré vendaNão
    Peso940g
    Autor para link
    Livro disponível - pronta entregaNão
    Dimensões23.49 x 19.05 x 2.84
    IdiomaInglês
    Tipo itemLivro Importado
    Número de páginas552
    Número da edição1ª EDIÇÃO - 2022
    Código Interno1148046
    Código de barras9781803246802
    AcabamentoPAPERBACK
    AutorJOSEPH, MANU
    EditoraPACKT PUBLISHING
    Sob encomendaSim

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