A Parameter Based Comparative Study of Deep Learning Algorithms for Stock Price Prediction

Abstract

Stock exchanges are places where buyers and sellers meet to trade shares in public companies. Stock exchanges encourage investment. Companies can grow, expand, and generate jobs in the economy by raising cash. These investments play a crucial role in promoting trade, economic expansion, and prosperity. We compare the three well-known deep learning algorithms, LSTM, GRU, and CNN, in this work. Our goal is to provide a thorough study of each algorithm and identify the best strategy when taking into account elements like accuracy, memory utilization, interpretability, and more. To do this, we recommend the usage of hybrid models, which combine the advantages of the various methods while also evaluating the performance of each approach separately. Aim of research is to investigate model with the highest accuracy and the best outcomes with respect to stock price prediction.

Novel Approach to Design a Model for Software Effort Estimation Using Linear Regression

“…Briefly introduce what Software Effort Estimation is, its importance in software development, and the general approaches (e.g., algorithmic models, expert judgment, machine learning) [19] [20]. The Historical Overview Provides a timeline or discussion of the evolution of SEE methods, highlighting key models and their impact on the field [21] [22].…”