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Found 9 papers, 5 papers with code
Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being.
LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics
The COVID-19 pandemic has accentuated socioeconomic disparities across various racial and ethnic groups in the United States.
Social media use among American Indians in South Dakota: Preferences and perceptions
Most of the participants reported that the use of social media increased tremendously during COVID-19 and had perceptions of more negative effects than positive effects.
DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks
Therefore, DG-Trans is equipped with dual abilities that extract spatiotemporal dependency and identify anomaly nodes affected by incidents while removing noise introduced by benign nodes.
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Deep learning's performance has been extensively recognized recently.
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction
In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework.
Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent.
