5 papers
End-to-End Learning for Partially-Observed Time Series with PyPOTS
Wenjie Du, Yiyuan Yang, Tianxiang Zhan +1
Partially-observed time series (POTS) is ubiquitous in real-world applications, yet most existing toolchains separate missing-value handling from downstream learning, which limits…
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook
Haoxin Liu, Harshavardhan Kamarthi, Zhiyuan Zhao +6
Time series analysis (TSA) is a longstanding research topic in the data mining community and has wide real-world significance. Compared to "richer" modalities such as language and…
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
Haoxin Liu, Shangqing Xu, Zhiyuan Zhao +8
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 wit…
TSI-Bench: Benchmarking Time Series Imputation
Wenjie Du, Jun Wang, Linglong Qian +12
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the communit…
Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives
Yiyuan Yang, Zheshun Wu, Yong Chu +3
Process mining, as a high-level field in data mining, plays a crucial role in enhancing operational efficiency and decision-making across organizations. In this survey paper, we de…