adv-alstm

Code for paper "Enhancing Stock Movement Prediction with Adversarial Training" IJCAI 2019

autoformer

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

awesome-ai-in-finance

🔬 A curated list of awesome machine learning strategies & tools in financial market.

bi-mamba4ts

A python implementation of the 2404.15772v1.pdf paper on arxiv.org

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

doubleadapt

The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.

fedformer

financial-data-analysis

informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

interregional-correlation-analysis-of-conronavirus-confirmed-cases-using-lstm

itransformer

Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting".

kaggle

kangmincho1

ltsf-linear

This is the official implementation for AAAI-23 Oral paper "Are Transformers Effective for Time Series Forecasting?"

ltsf-nlinear-cryptobot

NLinear-based Crypto Portfolio Management and Automated Trading

mamba

mambastock

MambaStock: Selective state space model for stock prediction

nonstationary_transformers

Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415

patchtst

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

pyraformer

qlib

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

s-d-mamba

Code for "Is Mamba Effective for Time Series Forecasting?"

shinhanai-competition

side-projects

Collection of side projects

stock-price-prediction-using-gan

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

stocknet-code

Code for stock movement prediction from tweets and historical stock prices.

stocknet-dataset

A comprehensive dataset for stock movement prediction from tweets and historical stock prices.

til

Today I Learned

time-series-library

A Library for Advanced Deep Time Series Models.

timegan

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019