Abstract:

In order to solve the problems of uncertainty, discontinuity, randomness and nonlinearity in stock price forecasting, a TRSSA-ELM ( Tent Random Walk Sparrow Optimization Algorithm-Extreme Learning Machine) stock price forecasting model is proposed. Firstly, adaptive Tent chaotic mapping and random walk strategy are used to improve the algorithm, which enhances the diversity and randomness of the population and improves the local and global optimization ability of the algorithm. Secondly, the performance of TRSSA( Tent Random Walk Sparrow Optimization Algorithm) is verified by using single peak, multi-peak and fixed multi-peak

test functions. Compared to SSA( Sparrow Optimization Algorithm), AO( Aquila Optimizer), POA( Pelican Optimization Algorithm) and GWO(Grey Wolf Optimizer), TRSSA algorithm has better convergence speed, accuracy and statistical properties. Finally, because the ELM ( Extreme Learning Machine) model randomly generates weights and thresholds, which reduces the prediction accuracy and generalization ability, TRSSA algorithm is applied to optimize the weights and thresholds of the ELM model, and the TRSSA-ELM model is tested in Sanan Optoelectronic stock data set. The experimental results show that TRSSA-ELM model has better prediction accuracy and stability than SSA-ELM, ELM, SVR(Support Vector Regression) and GBDT(Gradient Boosting Decision Tree).

Key words: stock price forecast, tent random walk sparrow optimization algorithm-extreme learning machine(TRSSA-ELM) forecasting model, adaptive tent chaotic mapping, random walk strategy