Empirical Evaluation on Stock Market Forecasting via Extreme Learning Machine

Stock market forecasting is a crucial element of financial systems, yet it may be tough to anticipate marketplace trends because of the loud & non-stationary nature of the data. Deep learning is known for extracting superior conceptual properties from massive amounts of actual data without relying upon previous information, which may be useful in predicting financial activities. This paper seeks to offer a deep learning system that automatically mines the statistic laws of material and directs stock market operations using bottom neural network models and empirical mode deconstruction. This basic pattern was discovered but then dissected using the data's typical timeframe. Data from stock market transactions were examined, PSO-optimized, and forecasted. Prediction efficiency can be increased by merging non stationary and exponential financial time series. If a specific trust level is met, the deep learning prediction, which is based on the study of enormous amounts of financial trade data, can determine the future trend and financial markets pricing. The empirical findings demonstrate the superior predictive efficiency of the EMD-based deep learner models.The aim of this paper is to conduct an empirical evaluation regarding stock market forecasting using extreme learning machine. A detailed research methodology has been discussed in this study to conduct the research efficiently. Moreover, a thorough practical analysis and discussion was also included along with statistical interpretation to get desired research outcomes.