Zobrazeno 1 - 10
of 374
pro vyhledávání: '"B Aditya"'
Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the real world. In this work, we propose TimerBed, the first comprehensive
Externí odkaz:
http://arxiv.org/abs/2411.06018
Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nat
Externí odkaz:
http://arxiv.org/abs/2410.00049
Autor:
Kamarthi, Harshavardhan, Sasanur, Aditya B., Tong, Xinjie, Zhou, Xingyu, Peters, James, Czyzyk, Joe, Prakash, B. Aditya
Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works,
Externí odkaz:
http://arxiv.org/abs/2407.02657
Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time serie
Externí odkaz:
http://arxiv.org/abs/2407.02641
Autor:
Du, Wenjie, Wang, Jun, Qian, Linglong, Yang, Yiyuan, Ibrahim, Zina, Liu, Fanxing, Wang, Zepu, Liu, Haoxin, Zhao, Zhiyuan, Zhou, Yingjie, Wang, Wenjia, Ding, Kaize, Liang, Yuxuan, Prakash, B. Aditya, Wen, Qingsong
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectiv
Externí odkaz:
http://arxiv.org/abs/2406.12747
Autor:
Liu, Haoxin, Kamarthi, Harshavardhan, Kong, Lingkai, Zhao, Zhiyuan, Zhang, Chao, Prakash, B. Aditya
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Due to the dynamic nature of time-series data, it is crucial to equip TSF models with out-of-distribution (OOD) generalization abilities, as historical training data and
Externí odkaz:
http://arxiv.org/abs/2406.09130
Autor:
Liu, Haoxin, Xu, Shangqing, Zhao, Zhiyuan, Kong, Lingkai, Kamarthi, Harshavardhan, Sasanur, Aditya B., Sharma, Megha, Cui, Jiaming, Wen, Qingsong, Zhang, Chao, Prakash, B. Aditya
Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely
Externí odkaz:
http://arxiv.org/abs/2406.08627
Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmission mechanisms of infectious diseases. However, they often suffer from
Externí odkaz:
http://arxiv.org/abs/2403.19852
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities while preserving computational efficiency. However, existing prompting
Externí odkaz:
http://arxiv.org/abs/2402.16132
Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant challenges for analysis and computation. In response, graph reduction
Externí odkaz:
http://arxiv.org/abs/2402.03358