131 citations · 245 across the 24 of their papers we have counts for
24 papers
AsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting
Xiachong Lin, Du Yin, Hao Xue +5
Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through a…
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
Xiachong Lin, Du Yin, Arian Prabowo +6
Building sensors are embedded in physical topology, spatial hierarchy, and operational context, yet existing forecasters often treat them as isolated time series or rely on fixed c…
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
Du Yin, Hao Xue, Jinliang Deng +4
In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal…
From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks
Du Yin, Hao Xue, Arian Prabowo +2
Existing traffic forecasting benchmarks assume a fixed sensor set, but real road-sensor networks grow continuously as the road network changes year by year. We introduce the XXLTra…
TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation
Wilson Wongso, Lihuan Li, Arian Prabowo +4
Generating high-fidelity synthetic GPS trajectories is increasingly important for applications in transportation, urban planning, and what-if scenario simulation, especially as pri…
Electric Vehicle Charging Load Modeling: A Survey, Trends, Challenges and Opportunities
Xiachong Lin, Arian Prabowo, Imran Razzak +4
The evolution of electric vehicles (EVs) is reshaping the automotive industry, advocating for more sustainable transportation practices. Accurately predicting EV charging behavior…
There is No "apple" in Timeseries: Rethinking TSFM through the Lens of Invariance
Arian Prabowo, Flora D. Salim
Timeseries foundation models (TSFMs) have multiplied, yet lightweight supervised baselines and even classical models often match them. We argue this gap stems from the naive import…
Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Hanlin Dong, Arian Prabowo, Hao Xue +5
Urban sensor networks need forecasts that are accurate, carry useful uncertainty, and refresh fast enough to act on as new readings arrive. These goals conflict: deterministic mode…
Embedding spatial context in urban traffic forecasting with contrastive pre-training
Matthew Low, Arian Prabowo, Hao Xue +1
Urban traffic forecasting is a commonly encountered problem, with wide-ranging applications in fields such as urban planning, civil engineering and transport. In this paper, we stu…
BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics
Xiachong Lin, Arian Prabowo, Imran Razzak +4
Incorporating AI technologies into digital infrastructure offers transformative potential for energy management, particularly in enhancing energy efficiency and supporting net-zero…
Exploring Capabilities of Time Series Foundation Models in Building Analytics
Xiachong Lin, Arian Prabowo, Imran Razzak +4
The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By levera…
BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
Arian Prabowo, Xiachong Lin, Imran Razzak +4
Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety. Additionally, they contribute significantly to global energy consumption, accou…
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Du Yin, Jinliang Deng, Shuang Ao +6
Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's perform…
XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation
Du Yin, Hao Xue, Arian Prabowo +2
Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal pa…
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
Xiachong Lin, Arian Prabowo, Imran Razzak +4
The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to…
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…
Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map
Wei Shao, Arian Prabowo, Sichen Zhao +2
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor d…
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Nan Gao, Hao Xue, Wei Shao +5
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to…
Flight Delay Prediction using Airport Situational Awareness Map
Wei Shao, Arian Prabowo, Sichen Zhao +6
The prediction of flight delays plays a significantly important role for airlines and travelers because flight delays cause not only tremendous economic loss but also potential sec…
COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
Arian Prabowo, Piotr Koniusz, Wei Shao +1
The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains…