An Efficient and Simplified Model for Forecasting using SRM

HAFIZ MUHAMMAD SHAHZAD ASIF ； MUHAMMAD FAISAL HAYAT ； TAUQIR AHMAD

《Mehran University Research Journal of Engineering and Technology》 33卷1期 (2014/01) Pp. a49-58

摘要

Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory), SRM (Structural Risk Minimization) Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression) for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building.

關鍵字

Structural Risk Minimization ； Support Vector Regression ； Statistical Learning Theory ； Sales Forecasting ； Support Vector Machine ； VC Dimension