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Neural architectures
Deep coverage of N-BEATS, N-HiTS, TFT, and architecture design principles Coverage of modern forecasting models and frameworks

Beyond forecasting
Includes classification, clustering, anomaly detection, and embeddings Primarily focused on forecasting applications

Self-supervised learning
Dedicated coverage of self-supervised learning techniques Covers modern forecasting approaches and workflows

Conformal prediction
Dedicated coverage for uncertainty estimation and prediction intervals Covers probabilistic forecasting methods

PyTorch & experimentation
PyTorch-first approach with modular code and rapid experimentation workflows Focuses on forecasting workflows using modern Python tools

Publisher ‏ : ‎ Packt Publishing
Publication date ‏ : ‎ May 29, 2026
Language ‏ : ‎ English
Print length ‏ : ‎ 606 pages
ISBN-10 ‏ : ‎ 1805128183
ISBN-13 ‏ : ‎ 978-1805128182
Item Weight ‏ : ‎ 2.26 pounds
Dimensions ‏ : ‎ 7.5 x 1.37 x 9.25 inches
Best Sellers Rank: #436,890 in Books (See Top 100 in Books) #12 in Stochastic Modeling #79 in Machine Theory (Books) #328 in Statistics (Books)
Customer Reviews: 4.4 4.4 out of 5 stars (17) var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) if (dpAcrHasRegisteredArcLinkClickAction !== true) dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, “allowLinkDefault”: true , function (event) if (window.ue) ); ); P.when(‘A’, ‘cf’).execute(function(A) A.declarative(‘acrStarsLink-click-metrics’, ‘click’, “allowLinkDefault” : true , function(event) if(window.ue) ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) ); );