A Quantum Tensor Network-Based Viewpoint for Modeling and Analysis of Time Series Data

TL;DR

This paper introduces a quantum physics-inspired tensor network approach for time series analysis that enhances interpretability and uncertainty quantification, outperforming existing white box models in change point detection and clustering.

Contribution

It presents a novel quantum tensor network framework that maps time series data into a Hamiltonian model, enabling better interpretability and uncertainty estimation.

Findings

Improved change point detection accuracy

Enhanced interpretability of time series models

Effective uncertainty quantification in analysis

Abstract

Accurate uncertainty quantification is a critical challenge in machine learning. While neural networks are highly versatile and capable of learning complex patterns, they often lack interpretability due to their ``black box'' nature. On the other hand, probabilistic ``white box'' models, though interpretable, often suffer from a significant performance gap when compared to neural networks. To address this, we propose a novel quantum physics-based ``white box'' method that offers both accurate uncertainty quantification and enhanced interpretability. By mapping the kernel mean embedding (KME) of a time series data vector to a reproducing kernel Hilbert space (RKHS), we construct a tensor network-inspired 1D spin chain Hamiltonian, with the KME as one of its eigen-functions or eigen-modes. We then solve the associated Schr{\"o}dinger equation and apply perturbation theory to quantify uncertainty, thereby improving the interpretability of tasks performed with the quantum tensor network-based model. We demonstrate the effectiveness of this methodology, compared to state-of-the-art ``white box" models, in change point detection and time series clustering, providing insights into the uncertainties associated with decision-making throughout the process.