The paper proposes Retrieval Augmented Forecasting (RAF) that augments time-series foundation models with retrieved similar series to improve forecasting accuracy across domains.
A time series is worth 64 words: Long-term forecasting with transformers
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AME-TS is a structure-guided sparse MoE foundation model for time series that aligns expert routing with series-level temporal descriptors to achieve strong accuracy-efficiency tradeoffs on GIFT-Eval while improving specialization stability.
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.
NeurDE learns the equilibrium closure within a kinetic solver to outperform larger neural models on long-term predictions of nonlinear conservation laws including shocks.
TraXion supplies a unified pre-training approach for multi-entity spatiotemporal event streams that outperforms task-specific baselines on mobility tasks and transfers unchanged to authentication logs and ICU mortality prediction.
citing papers explorer
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Retrieval Augmented Time Series Forecasting
The paper proposes Retrieval Augmented Forecasting (RAF) that augments time-series foundation models with retrieved similar series to improve forecasting accuracy across domains.
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AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
AME-TS is a structure-guided sparse MoE foundation model for time series that aligns expert routing with series-level temporal descriptors to achieve strong accuracy-efficiency tradeoffs on GIFT-Eval while improving specialization stability.
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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.
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Neural equilibria for long-term prediction of nonlinear conservation laws
NeurDE learns the equilibrium closure within a kinetic solver to outperform larger neural models on long-term predictions of nonlinear conservation laws including shocks.
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TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond
TraXion supplies a unified pre-training approach for multi-entity spatiotemporal event streams that outperforms task-specific baselines on mobility tasks and transfers unchanged to authentication logs and ICU mortality prediction.