ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
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DeMa is a dual-path delay-aware Mamba architecture that decomposes MTS into intra-series temporal and inter-series variate paths to achieve SOTA performance with linear complexity on forecasting, imputation, anomaly detection, and classification.
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Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
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DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis
DeMa is a dual-path delay-aware Mamba architecture that decomposes MTS into intra-series temporal and inter-series variate paths to achieve SOTA performance with linear complexity on forecasting, imputation, anomaly detection, and classification.