Approximate Order-Preserving Pattern Mining for Time Series

TL;DR

This paper introduces AOP-Miner, a novel algorithm for mining approximate order-preserving patterns in time series data, effectively capturing similar trends despite data noise.

Contribution

It proposes a new approximate pattern mining method based on (delta-gamma) distance and a pattern fusion strategy, improving trend detection in noisy time series.

Findings

AOP-Miner outperforms existing methods in accuracy.

It finds more similar trends in noisy data.

The approach effectively handles data noise.

Abstract

The order-preserving pattern mining can be regarded as discovering frequent trends in time series, since the same order-preserving pattern has the same relative order which can represent a trend. However, in the case where data noise is present, the relative orders of many meaningful patterns are usually similar rather than the same. To mine similar relative orders in time series, this paper addresses an approximate order-preserving pattern (AOP) mining method based on (delta-gamma) distance to effectively measure the similarity, and proposes an algorithm called AOP-Miner to mine AOPs according to global and local approximation parameters. AOP-Miner adopts a pattern fusion strategy to generate candidate patterns generation and employs the screening strategy to calculate the supports of candidate patterns. Experimental results validate that AOP-Miner outperforms other competitive methods and can find more similar trends in time series.