A collaborative multiplicative Holt-Winters forecasting approach with dynamic fuzzy-level component
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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English English English |
| Published: |
Multidisciplinary Digital Publishing Institute (MDPI)
2018
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/63927/1/63927_A%20Collaborative%20Multiplicative%20Holt-Winters_article.pdf http://irep.iium.edu.my/63927/2/63927_A%20Collaborative%20Multiplicative%20Holt-Winters_scopus.pdf http://irep.iium.edu.my/63927/13/63927_A%20collaborative%20multiplicative%20Holt-Winters%20forecasting%20approach%20with%20dynamic%20fuzzy-level%20component_WOS.pdf http://irep.iium.edu.my/63927/ http://www.mdpi.com/2076-3417/8/4/530 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|