Recent Trends in Modelling the Continuous Time Series Using Deep Learning - Mansura Habiba,Barak A. Pearlmutter,Mehrdad Maleki
-30% with code BOOKS
30-day return policy
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, Tran ... Full description
You May Also Like
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-30% with code BOOKS
In stock at our supplier
TOP
The 21st Century Brain: Cutting edge neuroscience to help us navigate the future
-30% with code BOOKS
In stock at our supplier
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-30% with code BOOKS
In stock at our supplier
TOP
AI Engineering: Building Applications with Foundation Models
-30% with code BOOKS
In stock at our supplier
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-30% with code BOOKS
In stock at our supplier
TOP
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
-30% with code BOOKS
In stock at our supplier
TOP
How To Think About AI: A Guide For The Perplexed
-30% with code BOOKS
In stock at our supplier
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-30% with code BOOKS
In stock at our supplier
TOP
How to Talk to AI: (And How Not To)
-30% with code BOOKS
In stock at our supplier
TOP
Why Machines Learn: The Elegant Math Behind Modern AI
-30% with code BOOKS
In stock at our supplier
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-30% with code BOOKS
In stock at our supplier
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-30% with code BOOKS
In stock at our supplier
TOP
The Art of Game Design: A Book of Lenses
-30% with code BOOKS
In stock at our supplier
TOP
How Linux Works: What Every Superuser Should Know
-30% with code BOOKS
In stock at our supplier
TOP
Hands-On Large Language Models: Language Understanding and Generation
Maarten Grootendorst, Jay Alammar
-30% with code BOOKS
In stock at our supplier
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-30% with code BOOKS
In stock at our supplier
TOP
Slanted 47 Digital Tools: Slanted Magazine
-30% with code BOOKS
In stock at our supplier
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-30% with code BOOKS
In stock at our supplier
TOP
Deep Learning: Foundations and Concepts
Christopher M. Bishop, Hugh Bishop
-30% with code BOOKS
In stock at our supplier
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.
More Information
| Author | Mansura Habiba, Barak A. Pearlmutter, Mehrdad Maleki |
|---|---|
| Publisher | Springer Nature Switzerland |
| Release year | 2026 |
| Cover type | Hardcover |
| EAN | 9783032180216 |