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In many scenarios, humans prefer a text-based representation of quantitativedata over numerical, tabular, or graphical representations. The attractivenessof textual summaries for complex data has inspired research on data-to-textsystems. While there are several data-to-text tools for time series, few ofthem try to mimic how humans summarize for time series. In this paper, wepropose a model to create human-like text descriptions for time series. Oursystem finds patterns in time series data and ranks these patterns based onempirical observations of human behavior using utility estimation. Our proposedutility estimation model is a Bayesian network capturing interdependenciesbetween different patterns. We describe the learning steps for this network andintroduce baselines along with their performance for each step. The output ofour system is a natural language description of time series that attempts tomatch a human's summary of the same data.

Human-like Time Series Summaries via Trend Utility Estimation