#irregular-sampling News & Analysis

3 articles tagged with #irregular-sampling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · May 286/10

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QuITE: Query-Based Irregular Time Series Embedding

Researchers introduce QuITE, a plug-and-play embedding module that enables standard machine learning models to effectively process irregularly-sampled time series data without interpolation or architectural redesign. The approach uses learnable query tokens and self-attention to handle irregular temporal patterns, demonstrating significant performance improvements across forecasting and classification tasks.

AINeutralarXiv – CS AI · May 285/10

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Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration

Researchers propose Under-Cali, a machine learning framework for forecasting irregular multivariate time series data in real-time online settings. The system uses uncertainty estimation and dual-expert calibration to maintain accuracy despite dynamic data distribution shifts, achieving improvements over existing methods with minimal computational overhead.

AINeutralarXiv – CS AI · May 126/10

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TIDES: Implicit Time-Awareness in Selective State Space Models

Researchers introduce TIDES, a new selective state space model architecture that combines the expressivity of input-dependent models like Mamba with the native irregular time-series handling of continuous-time models like S5. By moving input-dependence to the state matrix rather than the discretization step, TIDES maintains the physical meaning of time intervals while preserving per-token expressivity, achieving state-of-the-art results on time-series benchmarks.