Research paper / RESOURCE PROFILE

HATS: Hierarchical Graph Attention Network for Stocks

Hierarchical graph attention network predicting stock movement from relational data.

Research & Papers
arxiv.orgPublished Aug 7, 2019
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Screenshot unavailableResource information and source links remain available. HATS: Hierarchical Graph Attention Network for Stocks — reviewed source website screenshot
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Overview

Hierarchical graph attention network predicting stock movement from relational data.

Topics & capabilities

deep-learninggraph-neural-networkpaperstock-prediction

Publication information

Publication title
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Authors
Raehyun Kim, Chan Ho So, Minbyul Jeong, Sanghoon Lee, Jinkyu Kim, Jaewoo Kang
Published
Aug 7, 2019
Last publication revision
Nov 12, 2019
Subject areas
q-fin.ST, cs.AI, cs.CE
Publication source
arxiv.org · abs/1908.07999v3 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

https://arxiv.org/abs/1908.07999 ↗

Discovery references

References explain how this resource entered the collection. They do not represent an endorsement.

Explore related resources

Screenshot unavailableResource information and source links remain available. DeepIntoStreams/TSFM_Finance — reviewed source website screenshot
RepositoryChecked capture

DeepIntoStreams/TSFM_Finance

github.com

the official implementation of the paper “Re(visiting) Time Series Foundation Models in Finance”.

GitHub stars
31
Language
Jupyter Notebook
Code license
Apache-2.0
foundation-modeltime-seriesresearch-lab
Research & Papers Explore →
96 Authority 96 / 100 Cited by 2 resources in this directory 2 discovery sources Directory record updated: 2026-10-11 How scoring works →

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