Order-preserving pattern mining with forgetting mechanism

TL;DR

This paper introduces OPF-Miner, an algorithm for order-preserving pattern mining in time series that incorporates a forgetting mechanism to prioritize recent data, improving pattern discovery and clustering performance.

Contribution

It proposes a novel OPF-Miner algorithm with strategies to efficiently discover frequent order-preserving patterns using a forgetting mechanism.

Findings

OPF-Miner outperforms 12 alternative algorithms in experiments.

The forgetting mechanism enhances clustering performance of time series.

OPF-Miner effectively reduces redundant pattern calculations.

Abstract

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important; however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed.