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 (LT), growth rate (bT) and seasonal component (SNT). The current practice of integrating and/or determining the initial value of LT is a stationary process, as it restricts the scope of adjustment with the progression of time and, thereby, the forecasting accuracy is compromised, while the periodic updating of LT is avoided, presumably due to the computational complexity. To overcome this obstacle, a fuzzy logic-based prediction model is developed to evaluate LT dynamically and to embed its value into the conventional MHW approach. The developed model is implemented in the MATLAB Fuzzy Logic Toolbox along with an optimal smoothing constant-seeking program. The new model, proposed as a collaborative approach, is tested with real-life data gathered from a local manufacturer and also for two industrial cases extracted from literature. In all cases, a significant improvement in forecasting accuracy is achieved.
Highlights
In the contemporary world of business, due to frequent technological advancements and operational changes, competition, complexities and challenges have increased drastically [1]
The multiplicative Holt-Winters model for seasonal demand forecasting consists of an iterative process by incorporating the estimated level (LT ) defined as: L T = α( DT /seasonal parameter (SNT) −l ) + (1 − α)( L T −1 + bT −1 )
A scatter diagram of time series representing the demand for 750 KV transformer tanks as shown in Figure 3 reveals an overall uprising trend with multiplicative seasonality
Summary
In the contemporary world of business, due to frequent technological advancements and operational changes, competition, complexities and challenges have increased drastically [1]. To incorporate the effects of changing level, growth rate and seasonal pattern in forecasting, both of these Holt-Winters approaches practice recursive equations, the appropriateness of which depends largely on the initial values of the level, growth rate and seasonal components [14,18] In this regard, the initial values of the level and growth rate are usually estimated by using the least square regression method, the consideration of stationary mean adversely impacts the forecasting accuracy. The initial values of the level and growth rate are usually estimated by using the least square regression method, the consideration of stationary mean adversely impacts the forecasting accuracy It is computationally difficult and poses an even greater challenge to dynamically estimate and periodically update the values of the level (LT ) by modifying the conventional multiplicative Holt-Winters (CMHW) approach.
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