Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Kamarthi, Harshavardhan"'
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:
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
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
Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks u
Externí odkaz:
http://arxiv.org/abs/2311.11413
Providing accurate and reliable predictions about the future of an epidemic is an important problem for enabling informed public health decisions. Recent works have shown that leveraging data-driven solutions that utilize advances in deep learning me
Externí odkaz:
http://arxiv.org/abs/2311.07841
Autor:
Kamarthi, Harshavardhan, Kong, Lingkai, Rodríguez, Alexander, Zhang, Chao, Prakash, B. Aditya
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictio
Externí odkaz:
http://arxiv.org/abs/2310.11569
Autor:
Rodríguez, Alexander, Kamarthi, Harshavardhan, Agarwal, Pulak, Ho, Javen, Patel, Mira, Sapre, Suchet, Prakash, B. Aditya
The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being
Externí odkaz:
http://arxiv.org/abs/2207.09370
Autor:
Kamarthi, Harshavardhan, Kong, Lingkai, Rodríguez, Alexander, Zhang, Chao, Prakash, B. Aditya
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictio
Externí odkaz:
http://arxiv.org/abs/2206.07940