Forecasting GDP in china and efficient input interval

Authors:
Cui Yu-quan;Ma Li-jie;Xu Ya-peng
Affiliations:
School of Mathematics and System Sciences, Shandong University, Jinan, Shandong, China;School of Mathematics and System Sciences, Shandong University, Jinan, Shandong, China;Shandong University of Science and Technology, Taian, Shandong, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

Citing 4
Cited 0

Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control
Neural Information Processing: Research and Development

Neural Information Processing: Research and Development
Prediction of chaotic time series based on the recurrent predictor neural network

IEEE Transactions on Signal Processing
Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.02

Visualization

Abstract

In this paper we first give a new method of time series model to forecast GDP of China. The method proposed here aims to emphasis the importance of the impact of STEP-Affair on the GDP forecasting. The superiority of the method to ARMA model is illustrated in an example presented accordingly. Then in the system of whole economic when the GDP forecasted above is given, how can we allocate the limited resources to make the economical behavior relative efficient. We use data envelopment analysis to show how to determine input interval. Each input among the input interval as well as the given output constitute an efficient decision making unit (DMU). For decision makers the techniques are very important in decision making, especially in macroeconomic policies making in China.