Investigating Frank-Wolfe-based demand forecasting methods
With the growing emphasis on demand forecasting to ensure a company’s success and ability to make profits, it is imperative that businesses find a way to predict demand accu- rately to effectively allocate their resources and manpower. With the demand for demand forecasting predictive models, thi...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175567 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | With the growing emphasis on demand forecasting to ensure a company’s success and
ability to make profits, it is imperative that businesses find a way to predict demand accu-
rately to effectively allocate their resources and manpower. With the demand for demand
forecasting predictive models, this paper will explore further the novel Stochastic Subregion
Frank-Wolfe (SSRFW) Algorithm. By adjusting different parameters to run simulations,
this paper investigates how the SSRFW algorithm performs as a whole, as well as how its
performance may be affected by its parameters. Further, this paper also looks to compare
the SSRFW against a benchmark model to evaluate how well it does against other models.
Overall, this study finds that the SSRFW algorithm consistently performs well under differ-
ent scenarios. Though the effect of each parameter is examined during the investigation, it
is noted that more in-depth analysis can be done in the future to complement the existing
results. |
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