ALoT: A Time-Series Similarity Measure Based on Alignment of Textures

Abstract

Inferring the similarity between two time-series signals is a key step in several data analysis tasks for a variety of engineering applications. In this paper, we introduce a novel elastic similarity measure (ALoT) based on the alignment of textures, instead of observed values, extracted from input time-series signals. To obtain the texture information, Local Binary Patterns are adapted for one-dimensional signals. According to experiments performed on a large number of benchmark time-series classification datasets, the proposed method achieves higher accuracy than current pairwise similarity measures for several cases in a 1-Nearest Neighbor classification setup.