Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks

TL;DR

This paper introduces a neural network-based method for learning human-interpretable spatial-temporal logic formulas from data, demonstrated on COVID-19 and rain prediction datasets, with accuracy comparable to standard classifiers.

Contribution

It presents a novel neural network framework that learns weighted graph-based signal temporal logic formulas, incorporating user preferences and subformula structures.

Findings

Achieved classification accuracy comparable to baseline methods.

Successfully learned interpretable spatial-temporal properties from datasets.

Demonstrated applicability on COVID-19 and rain prediction data.

Abstract

Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (wGSTL) formulas. For learning wGSTL formulas, we introduce a flexible wGSTL formula structure in which the user's preference can be applied in the inferred wGSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible wGSTL formula structure. We initially train a neural network to learn the wGSTL operators and then train a second neural network to learn the parameters in a flexible wGSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, support vector machine, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods.