Regularized least squares fuzzy support vector regression for financial time series forecasting

被引：84
|
作者：
Khemchandani, Reshma [1 ]
Jayadeva [2 ]
Chandra, Suresh [1 ]
机构：
[1] Indian Inst Technol, Dept Math, New Delhi 110016, India
[2] Inst Area Vasant Kunj, IBM India Res Lab, New Delhi 110070, India
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 01期
关键词：
Machine learning; Support vector machines; Regression; Financial time series forecasting; Fuzzy membership; MACHINES;
D O I：
10.1016/j.eswa.2007.09.035
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a novel approach, termed as regularized least squares fuzzy support vector regression, to handle financial time series forecasting. Two key problems in. financial time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information, where relevance is related to recency in time. The approach requires only a single matrix inversion. For the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples. The efficacy of the proposed algorithm is demonstrated on. financial datasets available in the public domain. (C) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码：132 / 138
页数：7
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