Paper Info
Title
Discretization Method for the Detection of Local Extrema and Trends in Non-discrete Time Series.
Abstract
Mining, analysis and trend detection in time series is a very important problem for forecasting purposes. Manyresearchers have developed different methodologies applying techniques from different fields of science inorder to perform such analysis. In this paper, we propose a new discretization method that allows the detectionof local extrema and trends inside time series. The method uses sliding linear regression of specific timeintervals to produce a new time series from the angle of each regression line. The new time series producedallows the detection of local extrema and trends in the original time series. We have conducted several experimentson financial time series in order to discover trends as well as pattern and periodicity detection to forecastfuture behavior of Dow Jones Industrial Average 30 Index.
Year
Venue
Field
2015
ICEIS (3-1)
Data mining,Order of integration,Discretization,Computer science,Trend detection,Maxima and minima,Discrete time and continuous time,Pattern detection,Linear regression
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Konstantinos F. Xylogiannopoulos1187.74
Panagiotis Karampelas23415.16
Reda Alhajj31919205.67