Relevance feedback retrieval of time series data

Abstract

There has been much recent interest in retrieval of time series data. Earlier work has used a fixed similarity metric (e.g., Euclidean distance) to determine the similarity between a user-specified query and items in the database. Here, we describe a novel approach to retrieval of time series data by using relevance feedback from the user to adjust the similarity metric. This is important because the Euclidean distance metric does not capture many notions of similarity between time series. In particular, Euclidean distance is sensitive to various "distortions" such as offset translation, amplitude scaling, etc. Depending on the domain and the user, one may wish a query to be sensitive or insensitive to these distortions to varying degrees. This paper addresses this problem by introducing a profile that encodes the user's subjective notion of similarity in a domain. These profiles can be learned continuously from interaction with the user. We further show how the user profile may be embedded in a system that uses relevance feedback to modify the query in a manner analogous to the familiar text retrieval algorithms.