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Type: Artigo de periódico
Author: Ballini, R
Yager, RR
Abstract: 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.
Subject: Aggregation operators
smoothing techniques
time series
data mining
Country: Singapura
Editor: World Scientific Publ Co Pte Ltd
Rights: fechado
Identifier DOI: 10.1142/S0218488514500020
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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