Content Selection for Time Series Summarization Using Case-Based Reasoning

We propose a Case-Based Reasoning(CBR) approach for content selection, which is an intermediate step towards generating textual summaries of time series data in the weather prediction domain. Specifically, we handle two significant challenges, the first involving multivariate data that warrants modeling of the interaction of two ’channels’ (wind speed and direction in our context) and the second involving the effective integration of domain-specific knowledge in the form of rules with data from a case library of past instances of content selection. We present an approach that uses domain knowledge to transform a given raw time series instance into a representation that facilitates effective retrieval of relevant cases, which are then used for change point prediction. We empirically demonstrate that our approach combining CBR and domain rules outperforms classical content selection mechanisms that are based on rules or heuristics alone as well as those that are purely data-driven.