{"product_id":"recent-trends-in-modelling-the-continuous-time-series-using-deep-learning-von-mansura-habiba-barak-a-pearlmutter-und-mehrdad-maleki","title":"Recent Trends in Modelling the Continuous Time Series Using Deep Learning","description":"\n                                \n                \u003cp\u003eThis book presents the first unified, practical framework for continuous-time series analysis using state-of-the-art neural architectures. Moving beyond traditional discrete-time methods, it directly addresses real-world challenges such as irregular sampling, asynchronous observations, and hidden system dynamics through Neural ODEs, SDEs, and CDEs.\u003c\/p\u003e\n                                \n                \n                \u003cp\u003eCovering both foundational and advanced models — RNNs, Transformers, graph networks, and emerging quantum-hybrid approaches — the book bridges classical time-series theory with modern deep learning. It emphasizes probabilistic forecasting, uncertainty quantification, and cutting-edge generative techniques, including diffusion models and VAEs, equipping readers with tools for robust, interpretable predictions.\u003c\/p\u003e\n                                \n                \n                \u003cp\u003e\n                                        \n                    \u003cem\u003eRecent Trends in Modelling the Continuous Time Series using Deep Learning \u003c\/em\u003e\n                                        tackles core issues such as long-range dependencies, multivariate interactions, dimensionality reduction, and spatiotemporal coherence, while providing structured evaluation frameworks and benchmarking protocols tailored to continuous-time settings.\n                \n                \u003c\/p\u003e\n                                \n                \n                \u003cp\u003eThrough rich case studies in healthcare (EHR analytics, wearable monitoring), finance (volatility forecasting, high-frequency trading), and IoT systems (sensor fusion, predictive maintenance), the book demonstrates how continuous-time models enable personalized insights, constraint-aware learning, and more reliable decision-making. Designed for researchers, engineers, and practitioners, this book is a definitive resource for applying continuous-time neural methods to complex, real-world environments.\u003c\/p\u003e\n                            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783032180216\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Hardcover - 9783032180216","offer_id":58177645084997,"sku":"9783032180216","price":171.19,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/d17b31b9-4235-4e58-95b0-f8e659b2684f.jpg?v=1780720424","url":"https:\/\/shop.autorenwelt.de\/products\/recent-trends-in-modelling-the-continuous-time-series-using-deep-learning-von-mansura-habiba-barak-a-pearlmutter-und-mehrdad-maleki","provider":"Autorenwelt Shop","version":"1.0","type":"link"}