Publication
A Dynamic Mode Decomposition approach with Hankel blocks to forecast multi-channel temporal series
| dc.contributor.author | Vasconcelos Filho, Ênio | |
| dc.contributor.author | Lopes dos Santos, Paulo | |
| dc.date.accessioned | 2019-06-21T09:47:46Z | |
| dc.date.embargo | 2119 | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Forecasting is a task with many concerns, such as the size, quality, and behavior of the data, the computing power to do it, etc. This letter proposes the dynamic mode decomposition (DMD) as a tool to predict the annual air temperature and the sales of a stores’ chain. The DMD decomposes the data into its principal modes, which are estimated from a training data set. It is assumed that the data is generated by a linear time-invariant high order autonomous system. These modes are useful to find the way the system behaves and to predict its future states, without using all the available data, even in a noisy environment. The Hankel block allows the estimation of hidden oscillatory modes, by increasing the order of the underlying dynamical system. The proposed method was tested in a case study consisting of the long term prediction of the weekly sales of a chain of stores. The performance assessment was based on the best fit percentage index. The proposed method is compared with three neural network-based predictors. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1109/LCSYS.2019.2917811 | pt_PT |
| dc.identifier.issn | 2475-1456 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/14063 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Institute of Electrical and Electronics Engineers | pt_PT |
| dc.relation | UID CISTER, ref. UID/CEC/04234 | pt_PT |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8718360 | pt_PT |
| dc.subject | Dynamic mode decomposition | pt_PT |
| dc.subject | Hankel matrix | pt_PT |
| dc.subject | Orediction | pt_PT |
| dc.subject | System identification | pt_PT |
| dc.title | A Dynamic Mode Decomposition approach with Hankel blocks to forecast multi-channel temporal series | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 744 | pt_PT |
| oaire.citation.issue | 3 | pt_PT |
| oaire.citation.startPage | 739 | pt_PT |
| oaire.citation.title | IEEE Control Systems Letters | pt_PT |
| oaire.citation.volume | 3 | pt_PT |
| person.familyName | Vasconcelos Filho | |
| person.givenName | Ênio | |
| person.identifier.ciencia-id | AC16-F8BD-0A1D | |
| person.identifier.orcid | 0000-0001-5459-6821 | |
| person.identifier.rid | V-8255-2017 | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | d3dace3e-3d1c-419f-9243-31cfbcd03839 | |
| relation.isAuthorOfPublication.latestForDiscovery | d3dace3e-3d1c-419f-9243-31cfbcd03839 |