6 papers
Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru
Chuan Li, Jiang You, Hassine Moungla +3
Accurate modeling of human mobility is critical for understanding epidemic spread and deploying timely interventions. In this work, we leverage a large-scale spatio-temporal datase…
Dynamic Perturbed Adaptive Method for Infinite Task-Conflicting Time Series
Jiang You, Xiaozhen Wang, Arben Cela
We formulate time series tasks as input-output mappings under varying objectives, where the same input may yield different outputs. This challenges a model's generalization and ada…
Learning K-U-Net with constant complexity: An Application to time series forecasting
Jiang You, Arben Cela, René Natowicz +2
Training deep models for time series forecasting is a critical task with an inherent challenge of time complexity. While current methods generally ensure linear time complexity, ou…
Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon
Jiang You, Arben Cela, René Natowicz +2
Anomaly detection in time series data is a critical challenge across various domains. Traditional methods typically focus on identifying anomalies in immediate subsequent steps, of…
Robust Time Series Forecasting with Non-Heavy-Tailed Gaussian Loss-Weighted Sampler
Jiang You, Arben Cela, René Natowicz +2
Forecasting multivariate time series is a computationally intensive task challenged by extreme or redundant samples. Recent resampling methods aim to increase training efficiency b…
Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels
Jiang You, Arben Cela, René Natowicz +2
Time series forecasting task predicts future trends based on historical information. Transformer-based U-Net architectures, despite their success in medical image segmentation, hav…