| dc.creator | Ballini, R | |
| dc.creator | Yager, RR | |
| dc.date | 2014 | |
| dc.date | FEB | |
| dc.date | 2014-08-01T18:34:48Z | |
| dc.date | 2015-11-26T17:07:04Z | |
| dc.date | 2014-08-01T18:34:48Z | |
| dc.date | 2015-11-26T17:07:04Z | |
| dc.date.accessioned | 2018-03-28T23:55:32Z | |
| dc.date.available | 2018-03-28T23:55:32Z | |
| dc.identifier | International Journal Of Uncertainty Fuzziness And Knowledge-based Systems. World Scientific Publ Co Pte Ltd, v. 22, n. 1, n. 23, n. 40, 2014. | |
| dc.identifier | 0218-4885 | |
| dc.identifier | 1793-6411 | |
| dc.identifier | WOS:000332729900002 | |
| dc.identifier | 10.1142/S0218488514500020 | |
| dc.identifier | http://www.repositorio.unicamp.br/jspui/handle/REPOSIP/81077 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/81077 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1280016 | |
| dc.description | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
| dc.description | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description | In this paper, we investigate the use of weighted averaging aggregation operators as techniques for time series smoothing. We analyze the moving average, exponential smoothing methods, and a new class of smoothing operators based on linearly decaying weights from the perspective of ordered weights averaging to estimate a constant model. We examine two important features associated with the smoothing processes: the average age of the data and the expected variance, both defined in terms of the associated weights. We show that there exists a fundamental conflict between keeping the variance small while using the freshest data. We illustrate the flexibility of the smoothing methods with real datasets; that is, we evaluate the aggregation operators with respect to their minimal attainable variance versus average age. We also examine the efficiency of the smoothed models in time series smoothing, considering real datasets. Good smoothing generally depends upon the underlying method's ability to select appropriate weights to satisfy the criteria of both small variance and recent data. | |
| dc.description | 22 | |
| dc.description | 1 | |
| dc.description | 23 | |
| dc.description | 40 | |
| dc.description | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
| dc.description | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description | Multidisciplinary University Research Initiative (MURI) grant [W911NF-09-1-0392] | |
| dc.description | US Army Research Office (ARO) | |
| dc.description | ONR [N000141010121] | |
| dc.description | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
| dc.description | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description | Multidisciplinary University Research Initiative (MURI) grant [W911NF-09-1-0392] | |
| dc.description | ONR [N000141010121] | |
| dc.language | en | |
| dc.publisher | World Scientific Publ Co Pte Ltd | |
| dc.publisher | Singapore | |
| dc.publisher | Singapura | |
| dc.relation | International Journal Of Uncertainty Fuzziness And Knowledge-based Systems | |
| dc.relation | Int. J. Uncertainty Fuzziness Knowl.-Based Syst. | |
| dc.rights | fechado | |
| dc.source | Web of Science | |
| dc.subject | Aggregation operators | |
| dc.subject | smoothing techniques | |
| dc.subject | time series | |
| dc.subject | data mining | |
| dc.subject | Financial Decision-making | |
| dc.subject | Aggregation Operators | |
| dc.subject | Models | |
| dc.title | LINEAR DECAYING WEIGHTS FOR TIME SERIES SMOOTHING: AN ANALYSIS | |
| dc.type | Artículos de revistas | |
