Efficient Matrix Profile Computation Using Different Distance Functions

TL;DR

This paper introduces efficient algorithms for computing matrix profiles using various Euclidean distances, expanding beyond the traditional z-normalized Euclidean distance, and demonstrates their superior performance through experiments.

Contribution

The paper presents novel algorithms, AAMP and ACAMP, for fast matrix profile computation with non-normalized and z-normalized Euclidean distances, respectively.

Findings

AAMP is highly efficient for non-normalized Euclidean distances.

ACAMP outperforms SCRIMP++ for z-normalized Euclidean distance.

Algorithms show excellent performance in experiments.

Abstract

Matrix profile has been recently proposed as a promising technique to the problem of all-pairs-similarity search on time series. Efficient algorithms have been proposed for computing it, e.g., STAMP, STOMP and SCRIMP++. All these algorithms use the z-normalized Euclidean distance to measure the distance between subsequences. However, as we observed, for some datasets other Euclidean measurements are more useful for knowledge discovery from time series. In this paper, we propose efficient algorithms for computing matrix profile for a general class of Euclidean distances. We first propose a simple but efficient algorithm called AAMP for computing matrix profile with the "pure" (non-normalized) Euclidean distance. Then, we extend our algorithm for the p-norm distance. We also propose an algorithm, called ACAMP, that uses the same principle as AAMP, but for the case of z-normalized Euclidean distance. We implemented our algorithms, and evaluated their performance through experimentation. The experiments show excellent performance results. For example, they show that AAMP is very efficient for computing matrix profile for non-normalized Euclidean distances. The results also show that the ACAMP algorithm is significantly faster than SCRIMP++ (the state of the art matrix profile algorithm) for the case of z-normalized Euclidean distance.