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Modeling long- and short- term temporal patterns with deep neural networks

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it

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cs.LG 2

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2026 2

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representative citing papers

Benchmarking Sensor-Fault Robustness in Forecasting

cs.LG · 2026-05-11 · conditional · novelty 7.0

SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.

TempusBench: An Evaluation Framework for Time-Series Forecasting

cs.LG · 2026-04-13 · unverdicted · novelty 7.0

TempusBench is a new evaluation framework for time-series forecasting models that supplies fresh non-overlapping datasets, tasks beyond horizon and domain, consistent tuning across models, and visualization tools.

citing papers explorer

Showing 2 of 2 citing papers.

  • Benchmarking Sensor-Fault Robustness in Forecasting cs.LG · 2026-05-11 · conditional · none · ref 64

    SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.

  • TempusBench: An Evaluation Framework for Time-Series Forecasting cs.LG · 2026-04-13 · unverdicted · none · ref 41

    TempusBench is a new evaluation framework for time-series forecasting models that supplies fresh non-overlapping datasets, tasks beyond horizon and domain, consistent tuning across models, and visualization tools.