On the adoption of abductive reasoning for time series interpretation

TL;DR

This paper introduces an abductive reasoning approach for time series interpretation, aiming to overcome limitations of classification methods by generating explanations through conjectures organized hierarchically.

Contribution

It presents a novel abductive reasoning framework with algorithms for interpreting time series, demonstrated on ECG data, offering a new perspective beyond traditional classification.

Findings

Abductive reasoning effectively explains ECG patterns.

The approach outperforms traditional classification methods.

Hierarchical conjectures improve interpretability.

Abstract

Time series interpretation aims to provide an explanation of what is observed in terms of its underlying processes. The present work is based on the assumption that the common classification-based approaches to time series interpretation suffer from a set of inherent weaknesses, whose ultimate cause lies in the monotonic nature of the deductive reasoning paradigm. In this document we propose a new approach to this problem, based on the initial hypothesis that abductive reasoning properly accounts for the human ability to identify and characterize the patterns appearing in a time series. The result of this interpretation is a set of conjectures in the form of observations, organized into an abstraction hierarchy and explaining what has been observed. A knowledge-based framework and a set of algorithms for the interpretation task are provided, implementing a hypothesize-and-test cycle guided by an attentional mechanism. As a representative application domain, interpretation of the electrocardiogram allows us to highlight the strengths of the proposed approach in comparison with traditional classification-based approaches.