131 citations · 245 across the 24 of their papers we have counts for
5 papers · 1 filter
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
Arian Prabowo, Kaixuan Chen, Hao Xue +2
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment. However, this assumption bec…
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
Arian Prabowo, Kaixuan Chen, Hao Xue +2
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. Thi…
Message Passing Neural Networks for Traffic Forecasting
Arian Prabowo, Hao Xue, Wei Shao +2
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location. Tr…
Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
Arian Prabowo, Hao Xue, Wei Shao +2
New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads)…
Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
Arian Prabowo, Wei Shao, Hao Xue +2
Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the…