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Found 7 papers, 3 papers with code
Advancing Time Series Classification with Multimodal Language Modeling
In this work, we propose InstructTime, a novel attempt to reshape time series classification as a learning-to-generate paradigm.
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Secondly, the one-step generation schema is widely followed, which necessitates a customized forecasting head and overlooks the temporal dependencies in the output series, and also leads to increased training costs under different horizon length settings.
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture dynamic user preferences.
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests.
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks.
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Deep learning-based algorithms, e. g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task.
One Person, One Model--Learning Compound Router for Sequential Recommendation
The core idea of CANet is to route the input user behaviors with a light-weighted router module.
