Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering

TL;DR

This paper introduces a new dissimilarity measure called ranked Pearson correlation dissimilarity (RDPC) for time series clustering, which outperforms existing methods especially in complex cases with seasonal patterns and trends.

Contribution

The paper proposes the RDPC dissimilarity measure that combines element-wise differences with Pearson correlation, enhancing clustering performance on complex time series data.

Findings

RDPC outperforms existing clustering methods in complex scenarios.

The method effectively captures seasonal patterns, trends, and peaks.

Application to electricity consumption data demonstrates practical utility.

Abstract

Time series clustering is an unsupervised learning method for classifying time series data into groups with similar behavior. It is used in applications such as healthcare, finance, economics, energy, and climate science. Several time series clustering methods have been introduced and used for over four decades. Most of them focus on measuring either Euclidean distances or association dissimilarities between time series. In this work, we propose a new dissimilarity measure called ranked Pearson correlation dissimilarity (RDPC), which combines a weighted average of a specified fraction of the largest element-wise differences with the well-known Pearson correlation dissimilarity. It is incorporated into hierarchical clustering. The performance is evaluated and compared with existing clustering algorithms. The results show that the RDPC algorithm outperforms others in complicated cases involving different seasonal patterns, trends, and peaks. Finally, we demonstrate our method by clustering a random sample of customers from a Thai electricity consumption time series dataset into seven groups with unique characteristics.