Go back to publications View external

Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

April 2019 Shangeth Rajaa, Jajati Keshari Sahoo International Conference on Advances in Computing and Data Sciences (ICACDS 2019), Springer, pp. 349–359
#Deep Learning #Finance #CNN

Abstract

We propose a data-driven deep learning approach to predict future stock values using the feature extraction property of convolutional neural networks combined with Neural Arithmetic Logic Units. Rather than relying on technical indicators or sentiment analysis, the model learns directly from historical price data.

Cite as: Rajaa S., Sahoo J.K. (2019) Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction. In: Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore.