Trending repositories for topic forecasting

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.

Fast and Accurate ML in 3 Lines of Code

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

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...

A python library for user-friendly forecasting and anomaly detection on time series.

Platform for building AI that can learn and answer questions over federated data.

A unified framework for machine learning with time series

Scalable and user friendly neural :brain: forecasting algorithms.

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

Time series forecasting with machine learning models

Lightning ⚡️ fast forecasting with statistical and econometric models.

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Stock Price Prediction using Machine Learning Techniques

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Postgres with GPUs for ML/AI apps.

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

The tutorials for PyPOTS, guide you to model partially-observed time series datasets.

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Access to Sulie foundation models for time-series forecasting 📈

scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.

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...

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

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...

A unified multi-task time series model.

Time series forecasting with machine learning models

a Python toolbox loads 172 public time series datasets for machine/deep learning with a single line of code. Datasets from multiple domains including healthcare, financial, power, traffic, weather, an...

MOMENT: A Family of Open Time-series Foundation Models

Resources about time series forecasting and deep learning.

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...

Stock Price Prediction using Machine Learning Techniques

Scalable and user friendly neural :brain: forecasting algorithms.

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Unified Training of Universal Time Series Forecasting Transformers

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

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.

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

Fast and Accurate ML in 3 Lines of Code

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Unified Training of Universal Time Series Forecasting Transformers

Time series forecasting with machine learning models

A python library for user-friendly forecasting and anomaly detection on time series.

Statsmodels: statistical modeling and econometrics in Python

Scalable and user friendly neural :brain: forecasting algorithms.

Lightning ⚡️ fast forecasting with statistical and econometric models.

A unified framework for machine learning with time series

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in 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...

Time series forecasting with PyTorch

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...

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Postgres with GPUs for ML/AI apps.

Access to Sulie foundation models for time-series forecasting 📈

Wind Power Forecasting Based on Hybrid CEEMDAN-EWT Deep Learning Method

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...

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

The tutorials for PyPOTS, guide you to model partially-observed time series datasets.

Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}

Unified Training of Universal Time Series Forecasting Transformers

{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting

Time series forecasting with machine learning models

Easy to Deploy Prediction Market Platform

[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks

scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.

MOMENT: A Family of Open Time-series Foundation Models

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...

Datasets for time series forecasting

An estimation language

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.

A unified multi-task time series model.

Fast and Accurate ML in 3 Lines of Code

Platform for building AI that can learn and answer questions over federated data.

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

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

A python library for user-friendly forecasting and anomaly detection on time series.

A unified framework for machine learning with time series

Scalable and user friendly neural :brain: forecasting algorithms.

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Statsmodels: statistical modeling and econometrics in Python

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Lightning ⚡️ fast forecasting with statistical and econometric models.

Unified Training of Universal Time Series Forecasting Transformers

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...

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...

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

Time series forecasting with PyTorch

Time series forecasting with machine learning models

Postgres with GPUs for ML/AI apps.

Access to Sulie foundation models for time-series forecasting 📈

Materials for the Deploy and Monitor ML Pipelines with Python, Docker and GitHub Actions workshop at the PyData NYC 2024 conference

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024)

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...

Wind Power Forecasting Based on Hybrid CEEMDAN-EWT Deep Learning Method

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series to benchmark datasets from different domains

Easy to Deploy Prediction Market Platform

scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.

[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks

MOMENT: A Family of Open Time-series Foundation Models

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

Unified Training of Universal Time Series Forecasting Transformers

{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting

The tutorials for PyPOTS, guide you to model partially-observed time series datasets.

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour. This ...

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...

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

Automated Bayesian model discovery for time series data

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Unified Training of Universal Time Series Forecasting Transformers

A unified multi-task time series model.

MOMENT: A Family of Open Time-series Foundation Models

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024)

[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks

👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster

Materials for the Deploy and Monitor ML Pipelines with Python, Docker and GitHub Actions workshop at the PyData NYC 2024 conference

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...

Access to Sulie foundation models for time-series forecasting 📈

Platform for building AI that can learn and answer questions over federated data.

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.

Fast and Accurate ML in 3 Lines of Code

Postgres with GPUs for ML/AI apps.

A python library for user-friendly forecasting and anomaly detection on time series.

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.

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Scalable and user friendly neural :brain: forecasting algorithms.

Statsmodels: statistical modeling and econometrics in Python

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

A unified framework for machine learning with time series

Unified Training of Universal Time Series Forecasting Transformers

Lightning ⚡️ fast forecasting with statistical and econometric models.

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

Probabilistic time series modeling in Python

Time series forecasting with PyTorch

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

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

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.

A repository for COVID-19 factors and impacts on US economy presented at HICSS-55 Conference

Wind Power Forecasting Based on Hybrid CEEMDAN-EWT Deep Learning Method

This repo contains my work & The code base for this TensorFlow Developer specialization offered by deeplearning.AI

Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series to benchmark datasets from different domains

The fastest way to make and track predictions

Access to Sulie foundation models for time-series forecasting 📈

Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803

Performed comparative analysis of BiLSTM, CNN-BiLSTM and CNN-BiLSTM with attention models for forecasting cases.

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...

Automated Bayesian model discovery for time series data

Resources about time series forecasting and deep learning.

A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.

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...

Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}