| dc.creator | Luna, I | |
| dc.creator | Ballini, R | |
| dc.date | 2011 | |
| dc.date | JUL-SEP | |
| dc.date | 2014-08-01T18:21:32Z | |
| dc.date | 2015-11-26T17:07:10Z | |
| dc.date | 2014-08-01T18:21:32Z | |
| dc.date | 2015-11-26T17:07:10Z | |
| dc.date.accessioned | 2018-03-28T23:55:42Z | |
| dc.date.available | 2018-03-28T23:55:42Z | |
| dc.identifier | International Journal Of Forecasting. Elsevier Science Bv, v. 27, n. 3, n. 708, n. 724, 2011. | |
| dc.identifier | 0169-2070 | |
| dc.identifier | WOS:000292222900006 | |
| dc.identifier | 10.1016/j.ijforecast.2010.09.006 | |
| dc.identifier | http://www.repositorio.unicamp.br/jspui/handle/REPOSIP/77712 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/77712 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1280056 | |
| dc.description | This paper presents a data-driven approach applied to the long term prediction of daily time series in the Neural Forecasting Competition. The proposal comprises the use of adaptive fuzzy rule-based systems in a top-down modeling framework. Therefore, daily samples are aggregated to build weekly time series, and consequently, model optimization is performed in a top-down framework, thus reducing the forecast horizon from 56 to 8 steps ahead. Two different disaggregation procedures are evaluated: the historical and daily top-down approaches. Data pre-processing and input selection are carried out prior to the model adjustment. The prediction results are validated using multiple time series, as well as rolling origin evaluations with model re-calibration, and the results are compared with those obtained using daily models, allowing us to analyze the effectiveness of the top-down approach for longer forecast horizons. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. | |
| dc.description | 27 | |
| dc.description | 3 | |
| dc.description | 708 | |
| dc.description | 724 | |
| dc.language | en | |
| dc.publisher | Elsevier Science Bv | |
| dc.publisher | Amsterdam | |
| dc.publisher | Holanda | |
| dc.relation | International Journal Of Forecasting | |
| dc.relation | Int. J. Forecast. | |
| dc.rights | fechado | |
| dc.rights | http://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy | |
| dc.source | Web of Science | |
| dc.subject | Simulation | |
| dc.subject | Rule-based forecasting | |
| dc.subject | Forecasting competitions | |
| dc.subject | Disaggregation | |
| dc.subject | Fuzzy inference system | |
| dc.subject | Adaptive fuzzy systems | |
| dc.subject | Part 1 | |
| dc.subject | Identification | |
| dc.subject | Models | |
| dc.subject | Framework | |
| dc.title | Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting | |
| dc.type | Artículos de revistas | |
