Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Editore: Packt Publishing Limited, United Kingdom, Birmingham, 2024

Da: WorldofBooks, Goring-By-Sea, WS, Regno UnitoWorldofBooks

Venditore con 5 stelle

Venditore AbeBooks dal 16 marzo 2007

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Condizione: Usato - Molto buono

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Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Apply ML and global models to improve forecasting accuracy through practical examples Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions Book DescriptionPredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether youre working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, youll learn preprocessing, feature engineering, and model evaluation. As you progress, youll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills. *Email sign-up and proof of purchase requiredWhat you will learn Build machine learning models for regression-based time series forecasting Apply powerful feature engineering techniques to enhance prediction accuracy Tackle common challenges like non-stationarity and seasonality Combine multiple forecasts using ensembling and stacking for superior results Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series Evaluate and validate your forecasts using best practices and statistical metrics Who this book is forThis book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.…

Codice articolo GOR015111937

Dati bibliografici

Titolo
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
Autore
Manu Joseph Joseph, Manu
Editore
Packt Publishing Limited, United Kingdom, Birmingham
Anno di pubblicazione
2024
Condizione
Very Good
Rilegatura
Paperback
Peso dell'articolo
1102 grammi
Dimensioni
191.0 x 235.0

WorldofBooks

Goring-By-Sea, WS, Regno Unito

Venditore con 5 stelle

Venditore AbeBooks dal 16 marzo 2007

Tariffe di spedizione da Regno Unito a U.S.A.

ArticoloDa 7 a 12 giorni lavorativiDa 5 a 9 giorni lavorativi
Primo articoloEUR 6,51EUR 13,95
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In 2002, World of Books Group was founded on an ethos to do good, protect the planet and support charities by enabling more goods to be reused. Since then, we've grown into to a global company pioneering the circular economy. Today, we drive the circular economy through three re-commerce brands: - Wob: Through Wob, we sell. We provide affordable, preloved books and media to customers all over the world. A book leaves our collection of over seven million titles and begins a new chapter every two seconds, enabling more goods to be reused. - Ziffit: Through Ziffit, we buy. We give people around the world the opportunity to contribute to the circular economy, earn money and protect the planet, by trading their unwanted books and media. - Shopiago: Through Shopiago, we help others. By sharing the technology that has grown World of Books Group into the business it is today, we’re helping charities increase revenue and reduce waste through re-commerce.…

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World of Books Ltd

Mulberry House, Woods Way Trading Estate
Goring-By-Sea, Regno Unito BN12 4QY