Please use this identifier to cite or link to this item:

Title: Use of Partial Cumulative Sum to Detect Trends and Change Periods for Nonlinear Time Series
Authors: Wu, Berlin
Chen, Liyang
Contributors: 應數系
Keywords: fuzzy time series;change periods;partial cumulative sums;trend;noise
Date: 2006-07
Issue Date: 2015-10-02 16:50:49 (UTC+8)
Abstract: Because the structural change of a time series from one pattern to another may not switch at once but rather experience a period of adjustment, conventional change point detection may be inappropriate under some circumstances. Furthermore, changes in time series often occur gradually so that there is a certain amount of fuzziness in the change point. For this, considerable research has focused on the theory of change period detection for improved model performance. However, a change period in some small time interval may appear to be negligible noise in a larger time interval. In this paper, we propose an approach to detect trends and change periods with fuzzy statistics using partial cumulative sums. By controlling the parameters, we can filter the noises and discover suitable change periods. Having discovered the change periods, we can proceed to identify the trends in the time series. We use simulations to test our approach. Our results show that the performance of our approach is satisfactory.
Relation: 經濟與管理論叢(Journal of Economics and Management), 2(2), 123-145
Data Type: article
Appears in Collections:[應用數學系] 期刊論文

Files in This Item:

File Description SizeFormat
123-145.pdf243KbAdobe PDF545View/Open

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing