Physalia Forecasting Course
Ecological forecasting with R 📦's {mvgam} and {brms}
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Statsmodels | 9,242 |  | 3,328 | 2,135 | 3 months ago | 35 | May 05, 2023 | 2,716 | bsd-3-clause | Python |
| Statsmodels: statistical modeling and econometrics in Python | ||||||||||
| Sktime | 7,405 |  | 57 | 7 days ago | 57 | November 06, 2023 | 912 | bsd-3-clause | Python | |
| A unified framework for machine learning with time series | ||||||||||
| Tsai | 4,659 |  | 2 | a month ago | 47 | November 13, 2023 | 71 | apache-2.0 | Jupyter Notebook | |
| Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai | ||||||||||
| Flow Forecast | 1,759 | 3 months ago | 101 | gpl-3.0 | Python | |||||
| Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting). | ||||||||||
| Time Series Transformers Review | 1,519 | 8 months ago | 3 | mit | ||||||
| A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series. | ||||||||||
| Aeon | 723 |  | 3 | 3 months ago | 8 | October 05, 2023 | 167 | bsd-3-clause | Python | |
| A toolkit for conducting machine learning tasks with time series data | ||||||||||
| Awesome Ai For Time Series Papers | 627 | 8 months ago | mit | |||||||
| A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals. | ||||||||||
| Pypots | 558 | 3 months ago | 20 | November 30, 2023 | 14 | bsd-3-clause | Python | |||
| A Python toolbox/library for reality-centric machine learning/deep learning on partially-observed time series with PyTorch, including SOTA models supporting tasks of imputation, classification, clustering, and forecasting on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data. https://arxiv.org/abs/2305.18811 | ||||||||||
| Scinet | 509 | 10 months ago | 20 | apache-2.0 | Python | |||||
| The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022) | ||||||||||
| Time Series Forecasting With Python | 499 | a year ago | 7 | Jupyter Notebook | ||||||
| A use-case focused tutorial for time series forecasting with python | ||||||||||
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