2 citations · 6 across the 13 of their papers we have counts for
15 papers
SemReuse: Certified Reuse of Cached Natural-Language Views for Semantic Operators
Phat T. Tran-Truong, Tung Kieu, Xuan-Bach Le +4
Semantic operators allow users to filter unstructured data using conditions expressed in natural language, but existing systems typically invoke an LLM for every input tuple. Analy…
WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting
Yuhan Zhu, Xiangfei Qiu, Hanyin Cheng +5
Time series forecasting is typically framed as learning a direct mapping from historical to future observations in the observation space. However, sequences of observations general…
TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version
David Campos, Bin Yang, Tung Kieu +3
The ongoing digitization has led to a proliferation of time-series data streams that monitor a variety of processes, from which valuable insights may be obtained. Further, the emer…
DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks
Yan Lin, Jilin Hu, Shengnan Guo +3
Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segment…
TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation
Xingjian Wu, Junkai Lu, Zhengyu Li +5
Time series data widely exist in real-world cyber-physical systems. Though analyzing and interpreting them contributes to significant values, e.g, disaster prediction and financial…
Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting
Xiangfei Qiu, Liu Yang, Xiangyu Xu +9
Time series forecasting occurs in a range of financial applications providing essential decision-making support to investors, regulatory institutions, and analysts. Unlike multivar…
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
Xiangfei Qiu, Zhe Li, Wanghui Qiu +10
Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time…
Data Driven Decision Making with Time Series and Spatio-temporal Data
Bin Yang, Yuxuan Liang, Chenjuan Guo +1
Time series data captures properties that change over time. Such data occurs widely, ranging from the scientific and medical domains to the industrial and environmental domains. Wh…
RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems
Biao Ouyang, Yingying Zhang, Hanyin Cheng +6
With the continued migration of storage to cloud database systems,the impact of slow queries in such systems on services and user experience is increasing. Root-cause diagnosis pla…
EasyTime: Time Series Forecasting Made Easy
Xiangfei Qiu, Xiuwen Li, Ruiyang Pang +11
Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researche…
Fully Automated Correlated Time Series Forecasting in Minutes
Xinle Wu, Xingjian Wu, Dalin Zhang +4
Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on record…
TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
Zhe Li, Xiangfei Qiu, Peng Chen +8
Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF meth…
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Xiangfei Qiu, Jilin Hu, Lekui Zhou +8
Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly…
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
Yan Lin, Jilin Hu, Shengnan Guo +4
Vehicle movement is frequently captured in the form of GPS trajectories, i.e., sequences of timestamped GPS locations. Such data is widely used for various tasks such as travel-tim…
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Minbo Ma, Jilin Hu, Christian S. Jensen +4
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use o…