Deep Time Series Forecasting Models: A Comprehensive Survey
X Liu, W Wang - Mathematics, 2024 - mdpi.com
Deep learning, a crucial technique for achieving artificial intelligence (AI), has been
successfully applied in many fields. The gradual application of the latest architectures of …
successfully applied in many fields. The gradual application of the latest architectures of …
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting
Time series forecasting is crucial and challenging in the real world. The recent surge in
interest regarding time series foundation models, which cater to a diverse array of …
interest regarding time series foundation models, which cater to a diverse array of …
Spatiotemporal Fusion Transformer for large-scale traffic forecasting
The way humans travel and even their daily commute, is gradually expanding beyond the
confines of counties and cities. Traffic between counties, cities, and even across the entire …
confines of counties and cities. Traffic between counties, cities, and even across the entire …
Relation-Preserving Masked Modeling for Semi-Supervised Time-Series Classification
In this study, we address the challenge of label sparsity in time-series classification using
semi-supervised learning that effectively leverages numerous unlabeled instances. Our …
semi-supervised learning that effectively leverages numerous unlabeled instances. Our …
Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
Medical time series data, such as Electroencephalography (EEG) and Electrocardiography
(ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases …
(ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases …
Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing
We propose a transformer architecture for time series forecasting with a focus on time series
tokenisation and apply it to a real-world prediction problem from the pricing domain. Our …
tokenisation and apply it to a real-world prediction problem from the pricing domain. Our …
InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting
C Chi, X Wang, K Yang, Z Song, D Jin, L Zhu… - arXiv preprint arXiv …, 2024 - arxiv.org
Transformer has become one of the most popular architectures for multivariate time series
(MTS) forecasting. Recent Transformer-based MTS models generally prefer channel …
(MTS) forecasting. Recent Transformer-based MTS models generally prefer channel …