Computational aspects of the optimal transit path problem
In this paper we present a novel method for short term forecast of time series based on Knot-Optimizing Spline Networks (KOSNETS). The time series is first approximated by a nonlinear recurrent system. The resulting recurrent system is then approximated by feedforward B-spline networks, yielding a n...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
American Institute of Mathematical Sciences AIMS
2008
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| Online Access: | http://aimsciences.org/journals/jimo/contents.jsp http://hdl.handle.net/20.500.11937/3053 |
| _version_ | 1848744124847489024 |
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| author | Caccetta, Louis Loosen, Ian Rehbock, Volker |
| author_facet | Caccetta, Louis Loosen, Ian Rehbock, Volker |
| author_sort | Caccetta, Louis |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we present a novel method for short term forecast of time series based on Knot-Optimizing Spline Networks (KOSNETS). The time series is first approximated by a nonlinear recurrent system. The resulting recurrent system is then approximated by feedforward B-spline networks, yielding a nonlinear optimization problem. In this optimization problem, both the knot points and the coefficients of the B-splines are decision variables so that the solution to the problem has both optimal coefficients and partition points. To demonstrate the usefulness and accuracy of the method, numerical simulations and tests using various model and real time series are performed. The numerical simulation results are compared with those from a well-known regression method, MARS. The comparison shows that our method outperforms MARS for nonlinear problems. |
| first_indexed | 2025-11-14T05:56:29Z |
| format | Journal Article |
| id | curtin-20.500.11937-3053 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T05:56:29Z |
| publishDate | 2008 |
| publisher | American Institute of Mathematical Sciences AIMS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-30532017-01-30T10:28:16Z Computational aspects of the optimal transit path problem Caccetta, Louis Loosen, Ian Rehbock, Volker In this paper we present a novel method for short term forecast of time series based on Knot-Optimizing Spline Networks (KOSNETS). The time series is first approximated by a nonlinear recurrent system. The resulting recurrent system is then approximated by feedforward B-spline networks, yielding a nonlinear optimization problem. In this optimization problem, both the knot points and the coefficients of the B-splines are decision variables so that the solution to the problem has both optimal coefficients and partition points. To demonstrate the usefulness and accuracy of the method, numerical simulations and tests using various model and real time series are performed. The numerical simulation results are compared with those from a well-known regression method, MARS. The comparison shows that our method outperforms MARS for nonlinear problems. 2008 Journal Article http://hdl.handle.net/20.500.11937/3053 http://aimsciences.org/journals/jimo/contents.jsp American Institute of Mathematical Sciences AIMS fulltext |
| spellingShingle | Caccetta, Louis Loosen, Ian Rehbock, Volker Computational aspects of the optimal transit path problem |
| title | Computational aspects of the optimal transit path problem |
| title_full | Computational aspects of the optimal transit path problem |
| title_fullStr | Computational aspects of the optimal transit path problem |
| title_full_unstemmed | Computational aspects of the optimal transit path problem |
| title_short | Computational aspects of the optimal transit path problem |
| title_sort | computational aspects of the optimal transit path problem |
| url | http://aimsciences.org/journals/jimo/contents.jsp http://hdl.handle.net/20.500.11937/3053 |