Learning to generate explainable stock predictions using self-reflective large language models
Explaining stock predictions is generally a difficult task for traditional non-generative deep
learning models, where explanations are limited to visualizing the attention weights on …
learning models, where explanations are limited to visualizing the attention weights on …
A survey on diffusion models for time series and spatio-temporal data
Multimodal multiscale dynamic graph convolution networks for stock price prediction
Predicting directional future stock price movements is very challenging due to the complex,
stochastic, and evolving nature of the financial markets. Existing literature either neglects …
stochastic, and evolving nature of the financial markets. Existing literature either neglects …
MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time Correlations
Z Cao, J Xu, C Dong, P Yu, T Bai - Proceedings of the 33rd ACM …, 2024 - dl.acm.org
Stock price prediction has been a challenging problem due to non-stationary dynamics and
complex market dependencies. Existing work has two limitations: 1. Previous studies have …
complex market dependencies. Existing work has two limitations: 1. Previous studies have …
Entity-based Financial Tabular Data Synthesis with Diffusion Models
In the rapidly evolving financial industry, the adoption of synthetic tabular data is on the rise
to augment scarce data and facilitate data sharing. Existing synthetic tabular data generation …
to augment scarce data and facilitate data sharing. Existing synthetic tabular data generation …
EEGCiD: EEG Condensation Into Diffusion Model
J Chen, D Pi, X Jiang, F Gao, B Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Electroencephalography (EEG)-based applications in Brain-Computer Interfaces (BCIs),
neurological disease diagnosis, rehabilitation, and other areas rely on the utilization of …
neurological disease diagnosis, rehabilitation, and other areas rely on the utilization of …