Authors:
Konstantinos F. Xylogiannopoulos
1
;
Panagiotis Karampelas
2
and
Reda Alhajj
1
Affiliations:
1
University of Calgary, Canada
;
2
Hellenic Air Force Academy, Greece
Keyword(s):
Moving Linear Regression Angle, Linear Regression, Pattern Detection, Trend Detection, Local Extrema, Local Minimum, Local Maximum, Discretization.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Methodologies and Technologies
;
Operational Research
;
Sensor Networks
;
Signal Processing
;
Simulation
;
Soft Computing
Abstract:
Mining, analysis and trend detection in time series is a very important problem for forecasting purposes. Many
researchers have developed different methodologies applying techniques from different fields of science in
order to perform such analysis. In this paper, we propose a new discretization method that allows the detection
of local extrema and trends inside time series. The method uses sliding linear regression of specific time
intervals to produce a new time series from the angle of each regression line. The new time series produced
allows the detection of local extrema and trends in the original time series. We have conducted several experiments
on financial time series in order to discover trends as well as pattern and periodicity detection to forecast
future behavior of Dow Jones Industrial Average 30 Index.