A Robust Algorithm in Sequentially Selecting Subset Time Series Systems Using Neural Networks

J. H. W. Penm, T. J. Brailsford and R. D. Terrell

Journal of Time Series Analysis, 2000, vol. 21, issue 4, 389-412

Abstract: In this paper a numerically robust lattice‐ladder learning algorithm is presented that sequentially selects the best specification of a subset time series system using neural networks. We have been able to extend the relevance of multilayered neural networks and so more effectively model a greater array of time series situations. We have recognized that many connections between nodes in layers are unnecessary and can be deleted. So we have introduced inhibitor arcs, reflecting inhibitive synapses. We also allow for connections between nodes in layers which have variable strengths at different points of time by introducing additionally excitatory arcs, reflecting excitatory synapses. The resolving of both time and order updating leads to optimal synaptic weight updating and allows for optimal dynamic node creation/deletion within the extended neural network. The paper presents two applications that demonstrate the usefulness of the process.

Date: 2000
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/1467-9892.00190

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:21:y:2000:i:4:p:389-412

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().