Towards The Inductive Acquisition of Temporal Knowledge

TL;DR

This paper introduces TIM, a domain-independent inductive methodology for discovering uncertain temporal patterns from real-time observations, enabling qualitative predictions in symbolic domains beyond traditional quantitative time series analysis.

Contribution

The paper presents TIM, a novel inductive approach for extracting uncertain temporal patterns in symbolic domains, expanding beyond existing quantitative time series methods.

Findings

TIM effectively discovers uncertain temporal patterns from real-time data.

The methodology supports qualitative predictions in symbolic representations.

TIM is domain-independent and adaptable to various applications.

Abstract

The ability to predict the future in a given domain can be acquired by discovering empirically from experience certain temporal patterns that tend to repeat unerringly. Previous works in time series analysis allow one to make quantitative predictions on the likely values of certain linear variables. Since certain types of knowledge are better expressed in symbolic forms, making qualitative predictions based on symbolic representations require a different approach. A domain independent methodology called TIM (Time based Inductive Machine) for discovering potentially uncertain temporal patterns from real time observations using the technique of inductive inference is described here.