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- Title
LINEAR DECAYING WEIGHTS FOR TIME SERIES SMOOTHING: AN ANALYSIS.
- Authors
BALLINI, R.; YAGER, R. R.
- 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.
- Subjects
TIME series analysis; AGGREGATION operators; STATISTICAL smoothing; DATA mining; VARIANCES; MATHEMATICAL constants
- Publication
International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 2014, Vol 22, Issue 1, p23
- ISSN
0218-4885
- Publication type
Article
- DOI
10.1142/S0218488514500020