In many scenarios, humans prefer a text-based representation of quantitative data over numerical, tabular, or graphical representations. The attractiveness of textual summaries for complex data has inspired research on data-to-text systems. While there are several data-to-text tools for time series, few of them try to mimic how humans summarize for time series. In this paper, we propose a model to create human-like text descriptions for time series. Our system finds patterns in time series data and ranks these patterns based on empirical observations of human behavior using utility estimation. Our proposed utility estimation model is a Bayesian network capturing interdependencies between different patterns. We describe the learning steps for this network and introduce baselines along with their performance for each step. The output of our system is a natural language description of time series that attempts to match a human's summary of the same data.

中文翻译：

---

通过趋势效用估计的类人时间序列摘要

在许多情况下，与数字、表格或图形表示相比，人类更喜欢基于文本的定量数据表示。复杂数据的文本摘要的吸引力激发了对数据到文本系统的研究。虽然有多种用于时间序列的数据到文本工具，但很少有人试图模仿人类如何总结时间序列。在本文中，我们提出了一个模型来为时间序列创建类似人类的文本描述。我们的系统在时间序列数据中找到模式，并使用效用估计根据人类行为的经验观察对这些模式进行排序。我们提出的效用估计模型是一个贝叶斯网络，它捕捉不同模式之间的相互依赖性。我们描述了该网络的学习步骤，并介绍了基线及其每个步骤的性能。