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Found 6 papers, 3 papers with code
Toward Physics-guided Time Series Embedding
This theory enables us to bypass the parameterized embedding layer and directly employ physical reconstruction techniques to acquire a data embedding representation.
TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
In this paper, we begin by validating this theory through wavelet analysis and propose the Transformer-based TwinS model, which consists of three modules to address the non-stationary periodic distributions: Wavelet Convolution, Period-Aware Attention, and Channel-Temporal Mixed MLP.
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years.
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
In long-term time series forecasting (LTSF) tasks, an increasing number of models have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their dynamical structures.
Multimodal Recommender Systems: A Survey
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
