Toward Global Optimization of ANN Supported by Instance Selection for Financial Forecasting

Abstract

Artificial Neural Network (ANN) is widely used in the business to get on forecasting, but is often low performance for noisy data. Many techniques have been developed to improve ANN outcomes such as adding more algorithms, feature selection and feature weighting in input variables and modification of input case using instance selection. This paper proposes a Euclidean distance matrix approach to instance selection in ANN for financial forecasting. This approach optimizes a selection task for relevant instance. In addition, the technique improves prediction performance. In this research, ANN is applied to solve problems in forecasting a demand for corporate insurance. This research has compared the performance of forecasting a demand for corporate insurance through two types of ANN models; ANN and ISANN (ANN using Instance Selection supported by Euclidean distance metrics). Using ISANN to forecast a demand for corporate insurance is the most outstanding.