Multidimensional scaling method for complex time series feature classification based on generalized complexity-invariant distance

Abstract

In this paper, we propose a multidimensional scaling (MDS) method based on complexity-invariant distance (CID) and generalized complexity-invariant distance (GCID) to analyze and classify complex time series like traffic signals and financial stock indexes. Three types of simulation time series from the $$\upalpha $$ -map model, the 2D Henon map model and the Lozi map model as well as two real-world time series are used to illustrate the practicability of the proposed MDS method. Results from two traditional MDS and the MDS based on the mutual information are compared with the MDS based on CID and GCID, which demonstrate the proposed method is more effective and reasonable.