AdaMamba adds input-dependent frequency bases and a unified time-frequency forgetting gate to Mamba, yielding higher forecasting accuracy than prior methods on standard long-term time series benchmarks.
Segrnn: Segment recurrent neural network for long-term time series forecasting
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UNVERDICTED 5representative citing papers
TimeGuard employs channel-wise pool training initialized with time-aware criteria and distance-regularized loss selection to defend time series forecasting against backdoor attacks, improving robustness by 1.96x while keeping clean performance within 5%.
LbCNNM-MQR adds modified quantile regression and calibration to convolutional low-rank models to generate accurate prediction intervals for multi-step time series forecasting.
FinTSB introduces a benchmark addressing diversity, standardization, and real-world applicability gaps in financial time series forecasting evaluations.
A federated learning framework lets distributed weather sensors train shared deep learning models for forecasting and anomaly detection while keeping raw data private.
citing papers explorer
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AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
AdaMamba adds input-dependent frequency bases and a unified time-frequency forgetting gate to Mamba, yielding higher forecasting accuracy than prior methods on standard long-term time series benchmarks.
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TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
TimeGuard employs channel-wise pool training initialized with time-aware criteria and distance-regularized loss selection to defend time series forecasting against backdoor attacks, improving robustness by 1.96x while keeping clean performance within 5%.
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Convolutionally Low-Rank Models with Modified Quantile Regression for Interval Time Series Forecasting
LbCNNM-MQR adds modified quantile regression and calibration to convolutional low-rank models to generate accurate prediction intervals for multi-step time series forecasting.
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FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
FinTSB introduces a benchmark addressing diversity, standardization, and real-world applicability gaps in financial time series forecasting evaluations.
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Federated Weather Modeling on Sensor Data
A federated learning framework lets distributed weather sensors train shared deep learning models for forecasting and anomaly detection while keeping raw data private.