深層学習による継続時系列モデリングの最新トレンド Recent Trends in Modelling the Continuous Time Series Using Deep Learning

著者名：Habiba, Mansura/Pearlmutter, Barak A./Maleki, Mehrdad

Springer（2026/05/13発売）

• 言語：ENG
• ISBN：9783032180216
• eISBN：9783032180223

Description

This 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.

Covering 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.

Recent Trends in Modelling the Continuous Time Series using Deep Learning 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.

Through 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.

Table of Contents

Chapter 1: Introduction to Continuous Time Series.- Chapter 2: Different Neural Network Models For Time Series Processing.- Chapter 3 : Emerging Trends and Open Challenges in Time Series Modelling.- Chapter 4 : Neural Network Techniques in Continuous Time Series Prediction.- Chapter 5: Neural Network for Time Series Modelling.- Chapter 6: Probabilistic and Generative Approaches to Continuous Time Series forecasting.- Chapter 7: Quantum-Hybrid Neural Networks for Continuous Time Series.- Chapter 8: Model Evaluation and Benchmarking.- Chapter 9: Applications of Continuous Time Series Analysis.