Discretization Method for the Detection of Local Extrema and Trends in Non-discrete Time Series
Konstantinos F. Xylogiannopoulos, Panagiotis Karampelas, Reda Alhajj · 2015
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.