Norms induced from OWA operators
IEEE Transactions on Fuzzy Systems
A framework for use of imprecise categorization in developing intelligent systems
IEEE Transactions on Fuzzy Systems
Criteria satisfaction under measure based uncertainty
Fuzzy Optimization and Decision Making
Models to determine parameterized ordered weighted averaging operators using optimization criteria
Information Sciences: an International Journal
The probabilistic weighted average and its application in multiperson decision making
International Journal of Intelligent Systems
Exponential smoothing with credibility weighted observations
Information Sciences: an International Journal
On characterizing features of OWA aggregation operators
Fuzzy Optimization and Decision Making
An analytic approach to obtain the least square deviation OWA operator weights
Fuzzy Sets and Systems
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In this paper, an overview of the time series smoothing problem is provided and the role of data aggregation is described using a weighted average of the past observations. Two important features associated with this smoothing process are introduced. One is the average age of the data and other is the expected variance. It is noted how both of these are defined in terms of the associated weights. We show the fundamental conflict between trying to keep the variance small while simultaneously using the freshest data. We study the moving average and exponential smoothing methods. Focusing on the aggregation aspect of the data smoothing problem allows us to draw upon the work done with the ordered weighted averaging aggregation operators to suggest new methods for developing smoothing techniques. A new class of smoothing operators based on the use of linearly decaying weights is introduced and is shown to have some better features than either exponential smoothing or the moving average. Other classes of smoothing operators are introduced. We introduce the idea of an operational estimate that involves adjusting a smoothed value by other considerations.