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

arXiv: https://arxiv.org/abs/2505.02173 · HTML: https://mlsanity.com/paper/2505.02173/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 4 of 4)

- [On Clustering Time Series Using Euclidean Distance and Pearson Correlation](https://mlsanity.com/paper/1601.02213/) (2016)
- [Clustering performance analysis using a new correlation-based cluster validity index](https://mlsanity.com/paper/2109.11172/) (2021)
- [Co-clustering of time-dependent data via Shape Invariant Model](https://mlsanity.com/paper/2104.03083/) (2021)
- [A Bayesian cluster validity index](https://mlsanity.com/paper/2402.02162/) (2024)

## Built on by (most cited first, 1 of 1)

- [4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients](https://mlsanity.com/paper/2505.07702/) (2025)

## Abstract (arXiv)

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.