Alexander März

XGBoostLSS. An extension of XGBoost to probabilistic modelling

LightGBMLSS. An extension of LightGBM to probabilistic modelling

CatBoostLSS. An extension of CatBoost to probabilistic modelling

Hyper-Trees. Forecasting with Hyper-Trees

DGBM. Distributional Gradient Boosting Machines

Py-BoostLSS. An extension of Py-Boost to probabilistic modelling

ConvTS-Mixer. Time Series Extension of "Patches Are All You Need"

StatMixedML.

Lightgbm-monotone-quantile. Multiple quantiles estimation model maintaining non-crossing condition or monotone quantioe condition using Lightgbm

GPBoost. GPBoost is a software library for combining tree-boosting with Gaussian process and mixed effects models

modeltime.gluonts. GluonTS Deep Learning with Modeltime

MES_LSTM. A Hybrid Method of Exponential Smoothing and Recurrent Neural Networks for Multivariate Time Series Forecasting

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

spacetime. Code for SpaceTime 🌌⏱️. Proposed in Effectively Modeling Time Series with Simple Discrete State Spaces, ICLR 2023.

feature-shift. Python

ts2vec. A universal framework for learning timestamp-level representations of time series

UnsupervisedScalableRepresentationLearningTimeSeries. Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

ForecastPFN. Jupyter Notebook

GrowNet. Python

fortuna. A Library for Uncertainty Quantification.

pytorch-tsmixer. A pip-installable PyTorch implementation of TSMixer, providing an easy-to-use and efficient solution for time-series forecasting.

imodels. Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

alibi. Algorithms for monitoring and explaining machine learning models

darima. Distributed ARIMA Models

local_neural_transformations. Companion code for the self-supervised anomaly detection algorithm proposed in the paper "Detecting Anomalies within Time Series using Local Neural Transformations" by Tim Schneider et al.

pytorch-transformer-ts. Repository of Transformer based PyTorch Time Series Models

causalai. Salesforce CausalAI Library: A Fast and Scalable framework for Causal Analysis of Time Series and Tabular Data

CSDI. Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"

datasets. 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

DA-RNN. 📃 PyTorch Implementation of DA-RNN (arXiv:1704.02971)

SimTS_Representation_Learning. Python

htsf. Hierarchical Time Series Forecasting

pytorch_forward_forward. Implementation of Hinton's forward-forward (FF) algorithm - an alternative to back-propagation

nannyml. nannyml: post-deployment data science in python

nebullvm. Forward Forward Implementation