LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis
Pytorch implementation of TABL from Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis
This helps financial traders and analysts predict short-term stock market movements. It takes historical financial time series data, like order book data from the FI-2010 dataset, and outputs predictions on price direction. This is designed for quantitative traders or financial researchers looking to test advanced deep learning models for market forecasting.
No commits in the last 6 months.
Use this if you need to predict future price changes in financial markets using sophisticated attention-augmented neural networks.
Not ideal if you're looking for long-term investment strategies or a simple, interpretable model for general financial analysis.
Stars
8
Forks
3
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 16, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sktime/sktime
A unified framework for machine learning with time series
aeon-toolkit/aeon
A toolkit for time series machine learning and deep learning
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
Nixtla/mlforecast
Scalable machine 🤖 learning for time series forecasting.