Statistics for topic forecasting
RepositoryStats tracks 569,482 Github repositories, of these 184 are tagged with the forecasting topic. The most common primary language for repositories using this topic is Python (97). Other languages include: Jupyter Notebook (32), R (20)
Stargazers over time for topic forecasting
Most starred repositories for topic forecasting (view more)
Trending repositories for topic forecasting (view more)
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
A python library for user-friendly forecasting and anomaly detection on time series.
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/c...
Statsmodels: statistical modeling and econometrics in Python
The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for fo...
Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.
[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/c...
MOMENT: A Family of Open Time-series Foundation Models
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Statsmodels: statistical modeling and econometrics in Python
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for fo...
Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.
The tutorials for PyPOTS, guide you to model partially-observed time series datasets.
MOMENT: A Family of Open Time-series Foundation Models
[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Chronos: Pretrained (Language) Models for Probabilistic 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
The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for fo...
Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.
Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024)
Performed comparative analysis of BiLSTM, CNN-BiLSTM and CNN-BiLSTM with attention models for forecasting cases.
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
[ICML2024] Unified Training of Universal Time Series Forecasting Transformers
MOMENT: A Family of Open Time-series Foundation Models
Chronos: Pretrained (Language) 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.
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...
Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803
Chronos: Pretrained (Language) 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
Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.