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PyTorch implementation of "ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data" (AAAI 2025)
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are commi…
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
Official implementation for AAAI2025: AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
PyTorch implementation of "Drift doesn't Matter: Dynamic Decomposition with Dffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection" (NeurIPS 2023)
Let your Claude able to think
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
A Python-embedded modeling language for convex optimization problems.
Official implementation of our ICML 2023 paper "LinSATNet: The Positive Linear Satisfiability Neural Networks".
TuShare is a utility for crawling historical data of China stocks
Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
All Algorithms implemented in Python
PyTorch implementation of FactorVAE
Python toolkit for quantitative finance
[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"
A massively parallel, high-level programming language
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Code release for "Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction" https://arxiv.org/abs/2309.00073
A Collection of Variational Autoencoders (VAE) in PyTorch.
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting (AAAI2024)
Unofficial PyTorch implementation of FactorVAE
This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the …
Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803