TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
arXiv preprint arXiv:2308.11200 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
LbCNNM-MQR adds modified quantile regression and calibration to convolutional low-rank models to generate accurate prediction intervals for multi-step time series forecasting.
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
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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TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
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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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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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Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
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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.
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