Regularized least squares fuzzy support vector regression for financial time series forecasting |
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| Authors: | Reshma Khemchandani Suresh Chandra |
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| Affiliation: | 1. Department of Mathematics, Indian Institute of Technology, Hauz Khas, New Delhi 110 016, India;2. IBM India Research Lab, Block-C, Institutional Area Vasant Kunj, New Delhi 110 070, India;1. Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, PR China;2. College of Science China Agricultural University, Beijing 100083, PR China;3. College of Mathematics, Jilin University, Changchun 130012, PR China;1. School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China;2. School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China;1. Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, Delhi, India;2. Department of Mathematics, Indian Institute of Technology, Delhi, India |
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| Abstract: | 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. |
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