Similarity-Based Queries for Time Series Data

TL;DR

This paper introduces a set of linear Fourier-based transformations for time series similarity queries, enabling efficient processing using R-tree indexes and supporting operations like moving average and time warping.

Contribution

It proposes a novel set of transformations for time series similarity queries that are compatible with R-tree indexing, improving query efficiency over sequential scans.

Findings

Transformations support key operations like moving average and time warping.

Query processing with R-tree is competitive with exact match queries.

Algorithm significantly faster than sequential scanning.

Abstract

We study a set of linear transformations on the Fourier series representation of a sequence that can be used as the basis for similarity queries on time-series data. We show that our set of transformations is rich enough to formulate operations such as moving average and time warping. We present a query processing algorithm that uses the underlying R-tree index of a multidimensional data set to answer similarity queries efficiently. Our experiments show that the performance of this algorithm is competitive to that of processing ordinary (exact match) queries using the index, and much faster than sequential scanning. We relate our transformations to the general framework for similarity queries of Jagadish et al.