Towards a Better Forecasting Model for Economic Indices

This paper presents a study of neural network forecasting construction system. Forecasting, especially financial forecasting has been attracting researchers and practitioners for many years. Many experiments suggest that neural networks can outperform conventional models in most cases. However, due to the tedious and time consuming of neural networks training, some of models reported in literature did not go through comprehensive experiments. We propose a neural network forecasting construction system based on the study of manual training procedures. We hope that the system will free human beings from the tedious trialand-error procedures and thus more progress can be achieved for neural networks in financial applications.