Fisher Information Framework for Time Series Modeling

TL;DR

This paper introduces a Fisher information-based framework for time series prediction, utilizing a quantum-inspired Schrödinger-like equation and demonstrating its effectiveness on biological and simulated data.

Contribution

It develops a novel Fisher information framework for time series modeling, integrating quantum mechanics concepts and deriving data-driven hypotheses for improved prediction accuracy.

Findings

Effective prediction on ECG and delay-differential equation data

Derivation of Fisher information relations from quantum mechanics principles

Demonstrated numerical accuracy and efficiency of the model

Abstract

A robust prediction model invoking the Takens embedding theorem, whose \textit{working hypothesis} is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the \textit{working hypothesis} satisfy a time independent Schr\"{o}dinger-like equation in a vector setting. The inference of i) the probability density function of the coefficients of the \textit{working hypothesis} and ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the \textit{working hypothesis}, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: $(i)$ the Mackey-Glass delay-differential equation, $(ii)$ one ECG sample from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and $(iii)$ one ECG from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.