Statistics for topic forecasting
RepositoryStats tracks 598,732 Github repositories, of these 190 are tagged with the forecasting topic. The most common primary language for repositories using this topic is Python (100). Other languages include: Jupyter Notebook (34), R (20)
Stargazers over time for topic forecasting
Most starred repositories for topic forecasting (view more)
Trending repositories for topic forecasting (view more)
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
Statsmodels: statistical modeling and econometrics in Python
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
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 ca...
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
MOMENT: A Family of Open Time-series Foundation Models
A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
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 ca...
Statsmodels: statistical modeling and econometrics in Python
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
Platform for building AI that can learn and answer questions over federated data.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Wind Power Forecasting Based on Hybrid CEEMDAN-EWT Deep Learning Method
{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
Platform for building AI that can learn and answer questions over federated data.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
A pip-installable PyTorch implementation of TSMixer, providing an easy-to-use and efficient solution for time-series forecasting.
[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Unified Training of Universal Time Series Forecasting Transformers
MOMENT: A Family of Open Time-series Foundation Models
Platform for building AI that can learn and answer questions over federated data.
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
A python library for user-friendly forecasting and anomaly detection on time series.
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
MOMENT: A Family of Open Time-series Foundation Models
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.