dim-k

dim-k's Stars

• TimeEval/evaluation-paper

  Supporting material and website for the paper "Anomaly Detection in Time Series: A Comprehensive Evaluation"

  727

• goldmansachs/gs-quant

  Python toolkit for quantitative finance

  Language:Jupyter Notebook7.6k924

• nasa/NASTRAN-95

  Language:FORTRAN444220

• cleanlab/cleanlab

  The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

  Language:Python9.4k728

• lux-org/lux

  Automatically visualize your pandas dataframe via a single print! 📊 💡

  Language:Python5.1k364

• pycaret/pycaret

  An open-source, low-code machine learning library in Python

  Language:Jupyter Notebook8.8k1.8k

• eriklindernoren/ML-From-Scratch

  Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

  Language:Python23.8k4.6k

• mims-harvard/UniTS

  A unified multi-task time series model.

  Language:Python40652

• jsyoon0823/TimeGAN

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

  Language:Jupyter Notebook831259

• boxyhq/saas-starter-kit

  🔥 Enterprise SaaS Starter Kit - Kickstart your enterprise app development with the Next.js SaaS boilerplate 🚀

  Language:TypeScript3.3k736

• dockur/windows

  Windows inside a Docker container.

  Language:Shell17.1k1.5k

• mfrdixon/ML_Finance_Codes

  Machine Learning in Finance: From Theory to Practice Book

  Language:Jupyter Notebook1.7k475

• superduper-io/superduper

  Superduper: Integrate AI models and machine learning workflows with your database to implement custom AI applications, without moving your data. Including streaming inference, scalable model hosting, training and vector search.

  Language:Jupyter Notebook4.7k448

• polakowo/vectorbt

  Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.

  Language:Python4.2k609

• KalleBylin/temporal-fusion-transformers

  Language:Jupyter Notebook238

• JIANPING-LUO/machine-learning-for-finance

  Using Supervised machine learning methods (Decision Tree, Boosting, KNN, ANN, SVM) to trade stocks

  Language:Python96

• kennedyCzar/STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA

  Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

  Language:Jupyter Notebook12236

• probcomp/adev

  Haskell prototype to accompany the paper "ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs"

  Language:Haskell64

• yongghongg/stock-screener

  A collection of notebooks I used in my Medium articles.

  Language:Jupyter Notebook13252

• public-apis/public-apis

  A collective list of free APIs

  Language:Python312k33.4k

• raminmh/CfC

  Closed-form Continuous-time Neural Networks

  Language:Python852138

• dynamicslab/modified-SINDy

  Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data

  Language:Jupyter Notebook5019

• dynamicslab/SINDy-PI

  SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics

  Language:MATLAB12742