Learning time series models with inductive logic programming

• Type: Article in International Conference Proceedings Book
• Date: 2003

Authors

Alexessander Alves (Author) Other; Rui Carlos Camacho de Sousa Ferreira da Silva (Author) FEUP; Eugénio da Costa Oliveira (Author) FEUP

Conference proceedings International

Title: Proceedings de EUROPEAN SYMPOSIUM ON INTELLIGENT TECHNOLOGIES, HYBRID SYSTEMS AND THEIR IMPLEMENTATION ON SMART ADAPTIVE SYSTEMS, EUNITE 2003

Pages: 421-430

EUROPEAN SYMPOSIUM ON INTELLIGENT TECHNOLOGIES, HYBRID SYSTEMS AND THEIR IMPLEMENTATION ON SMART ADAPTIVE SYSTEMS

Oulu, Finlândia, 10 a 11 de Julho de 2003

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Computer science > Informatics

Other information

Authenticus ID: P-010-VET

Abstract (EN): : This paper reports on a set of proposals that make Inductive Logic Programming (ILP) systems adequate for inducing time series models. The proposals include an improvement in the ILP search process by the introduction of a statistical model validation step. We propose the definition of an adequate cost function based on the information criteria. The definition of the model evaluation step consists in an intuitive statistics that limits the minimum accepted performance of an induced hypothesis. The ILP system we used was provided with a library of background knowledge predicates adequate for time series problems. The proposals described in this paper can be applied to any agnostic learning problem. Preliminary experiments have shown that all these modifications make an ILP system adequate to induce time series models and increase the capability of model choice automation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10