Python Probabilistic-forecasting Resources

What is judgyprophet? judgyprophet is a Bayesian forecasting algorithm based on Prophet, that enables forecasting while using information known by the

EPro-PnP EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation In CVPR 2022 (Oral). [paper] Hanshen

Nixtla Statistical ⚡️ Forecast Lightning fast forecasting with statistical and econometric models StatsForecast offers a collection of widely used uni

A python toolbox/library for data mining on partially-observed time series, supporting tasks of forecasting/imputation/classification/clustering on incomplete multivariate time series with missing values.

PMR computer tutorials on HMMs (2021-2022) This is a repository for computer tutorials of Probabilistic Modelling and Reasoning (2021/2022) - a Univer

ProbAI 2021 - Probabilistic Programming and Variational Inference Tutorial with Pryo Day 1 (June 14) Slides Notebook: students_PPLs_Intro Notebook: so

DeepXF: Explainable Forecasting and Nowcasting with State-of-the-art Deep Neural Networks and Dynamic Factor Model Also, verify TS signal similarities

Bitcoin Realized Volatility Forecasting with GARCH and Multivariate LSTM Author: Chi Bui This Repository Repository Directory ├── README.md

Retrieval-Augmented Denoising Diffusion Probabilistic Models (wip) Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in P

scAR scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA

Covid-Tracker This is an interactive website that tracks, models and predicts CO

event-forecasting Event Forecasting Algorithms Theory Correlating events in comp

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).

Graph Data Science: an abstraction layer in Python for building knowledge graphs, integrated with popular graph libraries – atop Pandas, RDFlib, pySHACL, RAPIDS, NetworkX, iGraph, PyVis, pslpython, pyarrow, etc.

Decoupled Smoothing in Probabilistic Soft Logic Experiments for "Decoupled Smoothing in Probabilistic Soft Logic". Probabilistic Soft Logic Probabilis

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting Recent progress in neural forecasting instigated significant improvements in the

NeuralForecast is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate model benchmarks and SOTA models implemented in PyTorch and PyTorchLightning.

Energy-based GFlowNets Code for Generative Flow Networks for Discrete Probabilistic Modeling by Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Vo

Documentation A python package to adjust the bias of probabilistic forecasts/hindcasts using "Mean and Variance Adjustment" method. Read documentation

Open-Source Toolbox for Infrastructures Probabilistic Deterioration Modelling OpenIPDM is a MATLAB open-source platform that stands for infrastructure

Statistical_Modelling Statistical & Probabilistic Analysis of Store Sales, University Survey, & Manufacturing data Statistical Methods for Decision Ma

scikit-event-correlation Event Correlation and Changing Detection Algorithm Theo

Personal Finance Forecaster - An AI tool for forecasting personal expenses

A feature store is a data storage system for data science and machine-learning. It can store raw data and also transformed features, which can be fed straight into an ML model or training script.

Feature engineering and machine learning: together at last! Lambdo is a workflow engine which significantly simplifies data analysis by unifying featu

A competition for forecasting electricity demand at the country-level using a standard backtesting framework

nixtlats:Deep Learning for Time Series Forecasting [nikstla] (noun, nahuatl) Period of time. State-of-the-art time series forecasting for pytorch. Nix

AgentFormer This repo contains the official implementation of our paper: AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecast

Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting #Dataset The folder "Dataset" contains the dataset use in this work and m

1 MAGNN This repo is the official implementation for Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting. 1.1 The frame

JointDemandForecasting.py A framework for multi-step probabilistic time-series/demand forecasting models File stucture JointDemandForecasting contains

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series

Deep learning for time series forecasting Flow forecast is an open-source deep learning for time series forecasting framework. It provides all the lat

Supervised forecasting of sequential data in Python. Intro Supervised forecasting is the machine learning task of making predictions for sequential da

TIMEX TIMEX (referred in code as timexseries) is a framework for time-series-forecasting-as-a-service. Its main goal is to provide a simple and generi

AutoTS AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. There are dozens of forecasting mod

scikit-hts Hierarchical Time Series with a familiar API. This is the result from not having found any good implementations of HTS on-line, and my work

Deep Learning Time Series Forecasting List of state of the art papers focus on deep learning and resources, code and experiments using deep learning f

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

CSDI This is the github repository for the NeurIPS 2021 paper "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

📈 Automated Time Series Forecasting Background: This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to gene

Safe Local Motion Planning with Self-Supervised Freespace Forecasting By Peiyun Hu, Aaron Huang, John Dolan, David Held, and Deva Ramanan Citing us Yo

PyTorch implementation of 'Denoising Diffusion Probabilistic Models' This reposi

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

Minimal code and simple experiments to play with Denoising Diffusion Probabilist

LABES This is the code for EMNLP 2020 paper "A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised L

cfdm A Python reference implementation of the CF data model. References Compliance with FAIR principles Documentation https://ncas-cms.github.io/cfdm

pv_predict_unet-lstm Code for "Intra-hour Photovoltaic Generation Forecasting based on Multi-source Data and Deep Learning Methods." IEEE Transactions

Task-aware Joint CWS and POS (TCwsPos) This is the implementation of the final project of the course DDA6309 Probabilistic Graphical Models, The Chine

Bean Machine Overview Bean Machine is a probabilistic programming language for inference over statistical models written in the Python language using

timemachines Autonomous, univariate, k-step ahead time-series forecasting functions assigned Elo ratings You can: Use some of the functionality of a s

1 SNAS4MTF This repo is the official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting. 1.1 The frame

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

PEDPF Parameter Efficient Deep Probabilistic Forecasting (PEDPF) is a repository containing code to run experiments for several deep learning based pr

Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr

Probabilistic Deep Forecast PyTorch implementation of a paper, titled: Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Decoupled Spatial-Temporal Graph Neural Networks Code for our paper: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.

WaveGrad Implementation (PyTorch) of Google Brain's high-fidelity WaveGrad vocoder (paper). First implementation on GitHub with high-quality generatio

Diffusion Probabilistic Models This repository provides a reference implementation of the method described in the paper: Deep Unsupervised Learning us

Denoising Diffusion Probabilistic Models Jonathan Ho, Ajay Jain, Pieter Abbeel Paper: https://arxiv.org/abs/2006.11239 Website: https://hojonathanho.g

Diffusion Probabilistic Models for 3D Point Cloud Generation [Paper] [Code] The official code repository for our CVPR 2021 paper "Diffusion Probabilis

mlforecast Scalable machine learning based time series forecasting. Install PyPI pip install mlforecast Optional dependencies If you want more functio

ThymeBoost ThymeBoost combines time series decomposition with gradient boosting to provide a flexible mix-and-match time series framework for spicy fo

HyperSPN This repository contains code for the paper: HyperSPNs: Compact and Expressive Probabilistic Circuits "HyperSPNs: Compact and Expressive Prob

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

DeepTCN TensorFlow TensorFlow (Python) implementation of multivariate time series forecasting model introduced in Chen, Y., Kang, Y., Chen, Y., & Wang

Conformal time-series forecasting Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021. If you use our code in yo

BEAR Overview This repository contains code associated with the preprint A generative nonparametric Bayesian model for whole genomes (2021), which pro

Probabilistic Tensor Decomposition of Neural Population Spiking Activity Matlab (recommended) and Python (in developement) implementations of Soulat e

Across-animal odor decoding by probabilistic manifold alignment (NeurIPS 2021) This repository is the official implementation of aligned mixture of la

On Memorization in Probabilistic Deep Generative Models This repository contains the code necessary to reproduce the experiments in On Memorization in

xwrf A lightweight interface for reading in output from the Weather Research and Forecasting (WRF) model into xarray Dataset. The primary objective of

Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet) Our paper: https://arxiv.org/abs/2111.13324 We will release the complet

CryptoForecasting using Machine and Deep learning (Part 1) CryptoForecasting using Machine Learning The main aspect of predicting the stock-related da

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral] Learning to Disambiguate Strongly In

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

ETNA Time Series Library Predict your time series the easiest way Homepage | Documentation | Tutorials | Contribution Guide | Release Notes ETNA is an

Public_prob_regression_with_huber_distributions This repository contains the code used for the implementation of the paper "Probabilistic Regression w

zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl

Description Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Ti

pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se

pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se

htsprophet Hierarchical Time Series Forecasting using Prophet Credit to Rob J. Hyndman and research partners as much of the code was developed with th

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting Note: You can find here the accompanying seq2seq RNN forecas

Movement Primitives Movement primitives are a common group of policy representations in robotics. There are many different types and variations. This

"A Probabilistic Hard Attention Model For Sequentially Observed Scenes" Authors: Samrudhdhi Rangrej, James Clark Accepted to: BMVC'21 A recurrent atte

PyAF (Python Automatic Forecasting) PyAF is an Open Source Python library for Automatic Forecasting built on top of popular data science python module

NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and

A hands-off Anomaly Detection Library Table of contents What is Luminaire Quick Start Time Series Outlier Detection Workflow Anomaly Detection for Hig

pymc-learn: Practical Probabilistic Machine Learning in Python Contents: Github repo What is pymc-learn? Quick Install Quick Start Index What is pymc-

Long Range Probabilistic Forecasting in Time-Series using High Order Statistics This is the code produced as part of the paper Long Range Probabilisti

Demand-Forecasting Business Problem A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

smart-city-predictions System prognostyczny dedykowany dla danych inteligentnych miast Praca inżynierska realizowana przez Michała Stawikowskiego and

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows This is the official implementation of the ICCV 2021 Paper "Probabilistic Mono

Documentation OWPHydroTools GitHub pages documentation Motivation We developed OWPHydroTools with data scientists in mind. We attempted to ensure the

Predictive Auto-scaling with OpenStack Monasca Giacomo Lanciano*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14t

Forecasting with the Temporal Fusion Transformer Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invari

Jump Reward Inference for 1D Music Rhythmic State Spaces An implementation of the probablistic jump reward inference model for music rhythmic informat