Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

Abstract

Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indicators, sentiment analysis etc to predict the future stock price. In recent years, many researches have extensively used machine learning for predicting the stock behaviour. In this paper we propose data driven deep learning approach to predict the future stock value with the previous price with the feature extraction property of convolutional neural network and to use Neural Arithmetic Logic Units with it.

Publication
Springer, Singapore

Cite as :

Rajaa S., Sahoo J.K. (2019) Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction. In: Singh M., Gupta P., Tyagi V., Flusser J., Ören T., Kashyap R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore

Avatar
Shangeth Rajaa
Voice AI Researcher | Senior ML Scientist at Anyreach AI

Voice AI researcher specializing in Turn-Taking, Full-Duplex Spoken Dialogue Systems, and Multi-Modal Speech LLMs. 6+ years research experience. 7+ publications at Interspeech, ICASSP, NeurIPS, and PMLR.