A Mobile Computing-Friendly Stock Price Trend Prediction Model
Published in 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), 2024
Recommended citation: Z. Liu, C. -W. Sham and L. Ma, "A Mobile Computing-Friendly Stock Price Trend Prediction Model," 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), Kitakyushu, Japan, 2024, pp. 210-214, doi: 10.1109/GCCE62371.2024.10760840. https://ieeexplore.ieee.org/abstract/document/10760840
In recent years, stock price trend prediction has been a hot topic in the field of AI for finance. Meanwhile, with the constant fluctuations in financial markets, investors increasingly require real-time stock price trend prediction on their mobile devices. Existing stock mid-price prediction models based on limit order books are mostly deployed on servers or computers, with their complex network structures and large parameters unsuitable for mobile computing. In this paper, we design a lightweight stock mid-price prediction model based on the transformer and develop an application that can be deployed on Android phones to forecast stock price trends. Our model substantially achieved a maximum reduction of 82% in Parameters and over 90% reduction in memory consumption compared to state-of-the-art works. Recommended citation:
@INPROCEEDINGS{10760840, author={Liu, Zhihang and Sham, Chiu-Wing and Ma, Longyu}, booktitle={2024 IEEE 13th Global Conference on Consumer Electronics (GCCE)}, title={A Mobile Computing-Friendly Stock Price Trend Prediction Model}, year={2024}, volume={}, number={}, pages={210-214}, keywords={Performance evaluation;Computational modeling;Memory management;Predictive models;Market research;Transformers;Real-time systems;Servers;Mobile computing;Smart phones;Mobile Computing;Deep Learning;Vision Transformer;Limit Order Book;Mid-Price}, doi={10.1109/GCCE62371.2024.10760840}}