12 citations · 12 across the 5 of their papers we have counts for
6 papers · 1 filter
TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion
Yu Sun, Yuan Chang, Xiaohou Shi +1
Large-scale multivariate time series from heterogeneous IoT sensors demand accurate long-term forecasting for resource scheduling and predictive maintenance. While recent time seri…
VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
PengYu Chen, Shang Wan, Xiaohou Shi +3
Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems. Existing methods require training one specific model…
RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction
PengYu Chen, Xiaohou Shi, Yuan Chang +2
Anomaly prediction (AP) in multivariate time series (MTS) is crucial to ensure system dependability. Existing methods either focus solely on whether an anomaly is imminent without…
Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
Aobo Liang, Yan Sun, Xiaohou Shi +1
In the past few years, time series foundation models have achieved superior predicting accuracy. However, real-world time series often exhibit significant diversity in their tempor…
WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting
Aobo Liang, Yan Sun, Nadra Guizani
In recent years, Transformer-based models (Transformers) have achieved significant success in multivariate time series forecasting (MTSF). However, previous works focus on extracti…
Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting
Aobo Liang, Xingguo Jiang, Yan Sun +2
Long-term time series forecasting (LTSF) provides longer insights into future trends and patterns. Over the past few years, deep learning models especially Transformers have achiev…