Foundation Models
| Packages | Description | Paper |
|---|---|---|
| MOMENT | Open time-series foundation models from CMU/Auton Lab for forecasting, anomaly detection, classification, imputation, and embeddings. | Paper Link |
| IBM Granite TSFM | IBM’s open repository for time series foundation models, notebooks, demos, and TinyTimeMixer workflows. | IBM Docs |
| Timer / Sundial | THUML repository for large generative time series models such as Timer, Timer-XL, and Sundial. | Timer Paper |
| OpenLTM | Unified codebase for developing and evaluating large time-series models and recent LLM4TS implementations. | N/A |
| Time-LLM | Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. | Paper Link |
| MOMENT | From CMU. A family of open-source foundation models for general-purpose time-series analysis. | Paper Link |
| TimesFM | From Google. A time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. | Paper Link |
| Lag-Llama | A general-purpose foundation model for univariate probabilistic forecasting. | Paper Link |
| MOIRAI | From Salesforce. An encoder-only Transformer model, specialized in universal time-series forecasting. | Paper Link |
| TimeGPT | The authors claim it’s the first foundation model for time series forecasting. | Paper Link |
| Chronos | From Amazon. Chronos is a family of pretrained time series forecasting models based on language model architectures. | Paper Link |
GitHub Resources
| Resource | Description |
|---|---|
| Awesome Time Series | Broad GitHub collection of libraries, datasets, and resources across the time series ecosystem. |
| TS-PTMs Survey Repo | Survey companion repository summarizing time-series pre-trained models and their task coverage. |
| Awesome-time-series Papers | Paper-centric GitHub summary of forecasting, classification, anomaly detection, and benchmark datasets. |