The analysis of time-series data associated with modernday industrial operations and scientific experiments is now pushing both computational power and resources to their limits. In order to analyze the existing and (more importantly) future very large time series collections, new technologies and the development of more efficient and smarter algorithms are required. The two editions of the Interdisciplinary Time Series Analysis Workshop brought together data analysts from the fields of computer science, astrophysics, neuroscience, engineering, electricity networks, and music. The focus of these workshops was on the requirements of different applications in the various domains, and also on the advances in both academia and industry, in the areas of time-series management and analysis. In this paper, we summarize the experiences presented in and the results obtained from the two workshops, highlighting the relevant state-ofthe- art-techniques and open research problems.

中文翻译：

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第一届和第二届跨学科时间序列分析研讨会（ITISA）报告

对与现代工业运营和科学实验相关的时间序列数据的分析现在正在将计算能力和资源推向极限。为了分析现有和（更重要的是）未来非常大的时间序列集合，需要新技术和开发更高效、更智能的算法。跨学科时间序列分析研讨会的两个版本汇集了来自计算机科学、天体物理学、神经科学、工程、电力网络和音乐领域的数据分析师。这些研讨会的重点是不同领域中不同应用的需求，以及时间序列管理和分析领域的学术界和工业界的进步。在本文中，