2018
DOI: 10.3390/app8040530
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A Collaborative Multiplicative Holt-Winters Forecasting Approach with Dynamic Fuzzy-Level Component

Abstract: The adoption of forecasting approaches such as the multiplicative Holt-Winters (MHW) model is preferred in business, especially for the prediction of future events having seasonal and other causal variations. However, in the MHW model the initial values of the time-series parameters and smoothing constants are incorporated by a recursion process to estimate and update the level (L T), growth rate (b T) and seasonal component (SN T). The current practice of integrating and/or determining the initial value of L … Show more

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Cited by 21 publications
(16 citation statements)
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“…This function predicts future values based on historical time-based data using the AAA (Holt-Winters) version of the exponential smoothing (ETS) algorithm with the weights assigned to data variances over time in proportion to the terms of their geometric progression based on the following exponential scale {1, (1 − α), (1 − α)2, (1 − α)3, ...}. [36][37][38]. The predicted value is a continuation of the historical values in the specified target date, which should be a continuation of the timeline, using the basic equations for the Holt-Winters' multiplicative method [39].…”
Section: Methodsmentioning
confidence: 99%
“…This function predicts future values based on historical time-based data using the AAA (Holt-Winters) version of the exponential smoothing (ETS) algorithm with the weights assigned to data variances over time in proportion to the terms of their geometric progression based on the following exponential scale {1, (1 − α), (1 − α)2, (1 − α)3, ...}. [36][37][38]. The predicted value is a continuation of the historical values in the specified target date, which should be a continuation of the timeline, using the basic equations for the Holt-Winters' multiplicative method [39].…”
Section: Methodsmentioning
confidence: 99%
“…, ∞}. By taking a fully general approach, the FORECAST.ETS function is able to make the most of all of the members of its family and automatically choose the most effective method for a given dataset [56][57][58][59].…”
Section: Methodsmentioning
confidence: 99%
“…The Holt-Winters forecasting model is observed to outperform other techniques for the time series, having changing seasonality, mean, and growth rate [39]. It is an adaptive model that automatically recognizes changes in data patterns.…”
Section: Holt-winters Modelmentioning
confidence: 99%