AI for Time Series
CRC Press (Verlag)
978-1-041-01032-6 (ISBN)
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In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.
The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.
Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. Emadeldeen Eldele is an Assistant Professor at Khalifa University, UAE. Zhenghua Chen is a Senior Lecture (Associate Professor) at University of Glasgow, UK. Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia. Qingsong Wen is currently the Head of AI & Chief Scientist at Squirrel Ai Learning. Xiaoli Li is currently Head of the Information Systems Technology and Design (ISTD) Pillar at Singapore University of Technology and Design (SUTD).
1 Introduction 2 Fedformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting 3 Fredf: Learning to Forecast in the Frequency Domain 4 PPGF: Probability Pattern-Guided Time Series Forecasting 5 Unlocking the Power of Lstm for Long Term Time Series Forecasting 6 Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification 7 Diffusion Language-Shapelets for Semi-Supervised Time-Series Classification 8 Graph-Aware Contrasting for Multivariate Time-Series Classification 9 Dcdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection 10 Multivariate Anomaly Detection with Self-Learning Graph Convolutional Networks 11 Saits: Self-Attention-Based Imputation for Time Series
| Erscheint lt. Verlag | 22.5.2026 |
|---|---|
| Zusatzinfo | 86 Line drawings, black and white; 86 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-041-01032-X / 104101032X |
| ISBN-13 | 978-1-041-01032-6 / 9781041010326 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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