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Example notebooks for Statsmodels - 2021
AmazingQuant——为交易而生的智能投研Lab。包含策略组合研究服务、量化数据服务、指标计算服务、绩效分析服务四大功能模块。
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
A survey and paper list of current Diffusion Model for Time Series and SpatioTemporal Data with awesome resources (paper, application, review, survey, etc.).
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
A python package for time series forecasting with scikit-learn estimators.
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Resources about time series forecasting and deep learning.
This is the project for deep learning in stock market prediction.
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
🎲 Iterable dataset resampling in PyTorch
The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
A Fair and Scalable Time Series Forecasting Benchmark and Toolkit.
Temporal Pattern Attention for Multivariate Time Series Forecasting
A Library for Advanced Deep Time Series Models.
Time series forecasting with PyTorch
Unified Model Interpretability Library for Time Series
Implementation of the InterpretTime framework
PyTorch code for ICPR 2020 paper "DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting"
PyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"
Unofficial PyTorch Implementation of SUM-GAN from "Unsupervised Video Summarization with Adversarial LSTM Networks" (CVPR 2017)
Interpretability and explainability of data and machine learning models
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)