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Generative Adversarial Networks for Stock Market prediction
This repository contains a Generative Adversarial Network (GAN) model designed to predict the closing prices in the financial market. The GAN utilizes a combination of generator and discriminator networks to generate synthetic closing price data, which can be used for forecasting and analysis purposes.
code for EvGAN: Evidential Generative Adversarial Networks
This project utilizes Generative Adversarial Networks (GANs) to tackle the problem of credit card fraud detection. GANs are a powerful deep learning technique that can be used for generating synthetic data, which can be beneficial in situations with imbalanced datasets, such as fraud detection.
Text to Video of various PIB Press Releases using Artificial Intelligence / Machine Learning / Generative Adversarial Networks in English and 13 Regional Languages viz. Hindi, Urdu, Punjabi, Gujarati, Marathi, Telugu, Kannada, Malayalam, Tamil, Odia, Bengali, Assamese and Manipuri.
Portfolio optimization and back-testing.
Portfolio optimization with deep learning.
The official codes for the paper Supervised Anomaly Detection via a Class-Balanced Generative Adversarial Network
The source codes for the research project "Early Warning For Stocks With Abnormal Trading Conditions Based On Generative Adversarial Networks"
PyTorch unofficial implements `f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks ` paper.
Code to accompany the paper "Fin-GAN: Forecasting and Classifying Financial Time Series via Generative Adversarial Networks"
A股财务造假预测模型,难点在于如何洗数据和构造因子
The implementation of "modeling financial time-series with generative adversarial networks"
A Flask-based Fraud Detection ML System utilizes XGBoost for training and SMOTE for handling imbalanced data, achieving an F1 score of 0.92 on Kaggle dataset
We used generative adversarial networks (GANs) to do anomaly detection for time series data.
Anomaly detection using Generative Adversarial Network
Generating synthetic financial time series using Generative Adversarial Networks built on the Transformer architecture.
Exploration of a paper on stock price prediction using Generative Adversarial Networks
Implementation of a Generative Adversarial Network for anomaly detection task in (financial) time series data.
Generative Adversarial Network for Detecting Irregularities. Applying GANs to anomaly detection.
Using Generative Adversarial Networks (GANs) algorithm to detect outliers on tabular data
Keras implementations of Generative Adversarial Networks.
A PyTorch implementation of the hierarchical deep learning model as outlined in the paper "Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals".
Implementing a Generative Adversarial Network on the Stock Market
Transformer-based Conditional Generative Adversarial Network for Multivariate Time Series Generation (IWTA - PAKDD2023)
Public repository for article "Multivariate emulation of convective-scale numerical weather predictions with generative adversarial networks: a proof-of-concept"
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
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