An evaluation and comparison of time series clustering using technical indices

Abstract

This paper evaluates and compares several multivariate time series clustering methods by incorporating technical indicators into time series data for clustering. We have proposed a method for clustering time series data by calculating technical indicators used in the financial sector for time series data and then compressing them to two dimensions using UMAP for clustering on a two-dimensional plane. In this paper, we create new artificial datasets to evaluate and compare our proposed UMAP-based method (UMAP SC) with multivariate time series clustering methods: GAK k-means, k-Shape, and Soft-DTW k-means.