Authors:
Anupam Tarsauliya
;
Ritu Tiwari
and
Anupam Shukla
Affiliation:
ABV-IIITM, India
Keyword(s):
Simulated annealing, Threshold acceptance, Genetic, ANN, Time series, Financial forecast.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Programming
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
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.