Envío gratis siempre a todo México.
Haz clic para hacer zoom
Not Available
Manu Joseph
Modern Time Series Forecasting with Python
Cargando comentarios…
Comparte
$870
$783
Envío gratis siempre
Comentarios
Cargando el resumen…
Cargando comentarios…
product
1251849Modern Time Series Forecasting with Pythonhttps://www.gandhi.com.mx/modern-time-series-forecasting-with-python/phttps://gandhi.vtexassets.com/arquivos/ids/835986/8a2a783f-e5b6-449e-9068-7bebac2b7818.jpg?v=638336416758770000783870MXNPackt PublishingInStock/Ebooks/1242276Modern Time Series Forecasting with Python783870https://www.gandhi.com.mx/modern-time-series-forecasting-with-python/phttps://gandhi.vtexassets.com/arquivos/ids/835986/8a2a783f-e5b6-449e-9068-7bebac2b7818.jpg?v=638336416758770000InStockMXN99999DIEbook20229781803232041_W3siaWQiOiIzMTc5MTY1MS0zMTQ0LTRmNGItYmI0OC02Mjc2MmZmZmM0MzYiLCJsaXN0UHJpY2UiOjg3MCwiZGlzY291bnQiOjg3LCJzZWxsaW5nUHJpY2UiOjc4MywiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781803232041_<p><strong>Build real-world time series forecasting systems which scale to millions of time series by mastering and applying modern concepts in machine learning and deep learning</strong></p><h4>Key Features</h4><ul><li>Explore industry-tested machine learning techniques to forecast millions of time series</li><li>Get started with the revolutionary paradigm of global forecasting models</li><li>Learn new concepts by applying them to real-world datasets of energy forecasting</li></ul><h4>Book Description</h4><p>We live in a serendipitous era where the explosion in the quantum of data collected and renewed interest in data-driven techniques like machine learning (ML) has changed the landscape of analytics and with it time series forecasting. This book attempts to take you beyond the commonly used classical statistical methods like ARIMA and introduce to you the latest techniques from the world of ML.</p><p>The book is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics like ML and deep learning (DL), and rarely touched upon topics like global forecasting models, cross-validation strategies, and forecast metrics. We start off with the basics of data handling and visualization and classical statistical methods and very soon move on to ML and DL models for time series forecasting.</p><p>By the end of the book, which is filled with industry-tested tips and tricks, you will have mastery over time series forecasting and will have acquired enough skills to tackle problems in the real world.</p><h4>What you will learn</h4><ul><li>Learn how to manipulate and visualize time series data like a pro</li><li>Set strong baselines with popular models like ARIMA</li><li>Discover how time series forecasting can be cast as regression</li><li>Engineer features for machine learning models for forecasting</li><li>Explore the exciting world of ensembling and stacking models</li><li>Learn about the global forecasting paradigm</li><li>Understand and apply state-of-the-art deep learning models like N-BEATS, AutoFormer, and more</li><li>Discover multi-step forecasting and cross-validation strategies</li></ul><h4>Who This Book Is For</h4><p>The book is ideal 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. A prior understanding of machine learning or forecasting would help speed up the learning. For seasoned practitioners in machine learning and forecasting, the book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.</p><h4>Table of Contents</h4><ol><li>Introducing Time Series</li><li>Acquiring and Processing Time Series Data</li><li>Analyzing and Visualizing Time Series Data</li><li>Setting a Strong Baseline Forecast</li><li>Time Series Forecasting as Regression</li><li>Feature Engineering for Time Series Forecasting</li><li>Target Transformations for Time Series Forecasting</li><li>Forecasting Time Series with Machine Learning Models</li><li>Ensembling and Stacking</li><li>Global Forecasting Models</li><li>Introduction to Deep Learning</li><li>Building Blocks of Deep Learning for Time Series</li><li>Common Modelling Patterns for Time Series</li><li>Attention and Transformers for Time Series</li><li>Strategies for Global Deep Learning Forecasting Models</li><li>Specialized Deep Learning Architectures for Forecasting</li><li>Multi-Step Forecasting</li><li>Evaluating Forecasts Forecast Metrics</li><li>Evaluating Forecasts Validation Strategies</li></ol>...(*_*)9781803232041_<p><b>Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts</b></p><h2>Key Features</h2><ul><li>Explore industry-tested machine learning techniques used to forecast millions of time series</li><li>Get started with the revolutionary paradigm of global forecasting models</li><li>Get to grips with new concepts by applying them to real-world datasets of energy forecasting</li></ul><h2>Book Description</h2>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. Youll 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 youll 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, youll be able to build world-class time series forecasting systems and tackle problems in the real world.<h2>What you will learn</h2><ul><li>Find out how to manipulate and visualize time series data like a pro</li><li>Set strong baselines with popular models such as ARIMA</li><li>Discover how time series forecasting can be cast as regression</li><li>Engineer features for machine learning models for forecasting</li><li>Explore the exciting world of ensembling and stacking models</li><li>Get to grips with the global forecasting paradigm</li><li>Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer</li><li>Explore multi-step forecasting and cross-validation strategies</li></ul><h2>Who this book is for</h2><p>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.</p>...9781803232041_Packt Publishinglibro_electonico_2c5fda2f-f3b8-3681-89ee-ba6bc44fefc3_9781803232041;9781803232041_9781803232041Manu JosephInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-fb85ebb3-ab6a-41c0-977b-807b70bff6ca.epub2022-11-24T00:00:00+00:00Packt Publishing