Search Packages

gluonts

Probabilistic time series modeling in Python

chronos-forecasting

Chronos: Pretrained Models for Time Series Forecasting

hierarchicalforecast

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

nixtla

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

skforecast

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

eland

Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

fev

Forecast evaluation library

tabpfn-time-series

Zero-shot Time Series Forecasting with TabPFN (work accepted at NeurIPS 2024 TRL and TSALM workshops)

momentfm

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

nixtlats

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

timecopilot-chronos-forecasting

Chronos: Pretrained Models for Time Series Forecasting

fabba

An efficient symbolic time series approximation with ABBA.

timecast-nightly

Performant, composable online learning

itransformer

Unofficial implementation of iTransformer - SOTA Time Series Forecasting using Attention networks, out of Tsinghua / Ant group

pipelinets

⚡ Simplest TimeSeries forecasting + AutoML. Preprocess → Predict.

spinests

spinesTS, a powerful toolset for time series prediction, is one of the cornerstones of PipelineTS.

hybridts

Hybrid time series forecasting library combining Prophet with gradient boosting models

apdtflow

APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily

expsmoothing

A simple introduction to statistical learning for time-series forecasting using the Holt Simple Exponential Smoothing method

padelf

This PyPi (pip) package gives you access to the most well-known time series datasets for electric load forecasting (and related tasks)

simba-ml

Simulation-Based Machine Learning

pytorchtime

A time series package for PyTorch

py-ssa-lib

Py-SSA-Lib: Python implementation of the multichannel singular spectrum analysis (MSSA) and singular spectrum analysis (SSA)

multimodal-timesfm

Multimodal extension of Google's TimesFM for time series forecasting with text