The use of domain knowledge in feature construction for financial time series prediction

• Type: Article in International Conference Proceedings Book
• Date: 2001

Authors

De Almeida, P (Author) Other; Torgo, L (Author) FEP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 116-129

10th Portuguese Conference on Artificial Intelligence, EPIA 2001

Porto, 17 December 2001 through 20 December 2001

Indexing

ISI Web of Knowledge

Scopus - 0 Citations

Other information

Authenticus ID: P-008-6W3

DOI: 10.1007/3-540-45329-6_15

Abstract (EN): Most of the existing data mining approaches to time series prediction use as training data an embed of the most recent values of the time series, following the traditional linear auto-regressive methodologies. However, in many time series prediction tasks the alternative approach that uses derivative features constructed from the raw data with the help of domain theories can produce significant prediction accuracy improvements. This is particularly noticeable when the available data includes multivariate information although the aim is still the prediction of one particular time series. This latter situation occurs frequently in financial time series prediction. This paper presents a method of feature construction based on domain knowledge that uses multivariate time series information. We show that this method improves the accuracy of next-day stock quotes prediction when compared with the traditional embed of historical values extracted from the original data. © Springer-Verlag Berlin Heidelberg 2001.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14