FINANCIAL TIME SERIES FORECAST USING SIMULATED ANNEALING AND THRESHOLD ACCEPTANCE GENETIC BPA NEURAL NETWORK

摘要：

Financial time series forecast has been eyed as key standard job because of its high non-linearity and high volatility in data. Various statistical methods, machine learning and optimization algorithms has been widely used for forecasting time series of various fields. To overcome the problem of solution trapping in local minima, here in this paper, we propose novel approach of financial time series forecasting using simulated annealing and threshold acceptance genetic back propagation network to obtain the global minima and better accuracy. Time series dataset is normalized and bifurcated into training and test datasets, which is used as supervised learning in BPA artificial neural network and optimized with genetic algorithm. Results thus obtained are used as seed for start point of simulated annealing and threshold acceptance. Empirical results obtained from proposed approach confirm the outperformance of forecast results than conventional BPA artificial neural networks.

关键词： Simulated annealing Threshold acceptance Genetic ANN Time series Financial forecast

作者: Anupam Tarsauliya Ritu Tiwari Anupam Shukla

作者单位: Soft Computing and Expert System Laboratory, ABV-IIITM, Gwalior, 474010, India

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 2639-2644

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）