Analysis of Stock Market Prediction for Future Trends Using Machine Learning

This paper addresses the stock trend prediction that emphases on Fintech system. Effective stock profiling is challenging due to non-Standard Dynamics and complex stock market interactions. The bulk of approaches now in use either search for relatively simple patterns with homogeneous basics or address each material separately. In reality, there are numerous potential sources for stock market connections, and complex graphs may conceal many underlying links. To comprehend the regularities of dynamic transitions in the stock market, an advanced Hierarchical Adaptive Temporal-Relational Interaction (HATR-I) model for cascading dilated convolutions and gating pathways is presented. The semantic level is utilized to check the interrelated stock data using an enhanced community process with temporal attenuation, which is based on adjacency graphs with edge properties. To further enhance the quality of the suggested model, Multi-scale Sequence Aggregation and Soft Clustering Regularization techniques are employed. Finally, we use regularized global cluster representation to improve stock representations. Based on three real stock market datasets, experimental proof of the effectiveness of our proposed model is shown in this paper.