How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Official implementation: AutoTimes: Autoregressive Time Series Forecasters via Large Language Models. [[Slides]](https://cloud.tsinghua.edu.cn/f/7689d30f92594ded84f0/), [[Poster]](https://cloud.tsinghua.edu.cn/f/f2c18ae34fef4e74ad46/)
The main features of thuml/autotimes are: Forecasting Models, Time Series Foundation Models.
Projects with overlapping indexed features include: salesforceairesearch/uni2ts. yuqinie98/patchtst. time-series-foundation-models/lag-llama — Lag-llama is a probabilistic machine learning foundation model designed for time series forecasting. It generates… lyhue1991/eat_tensorflow2_in_30_days — This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow.… adityalab/lstprompt — Implementation of the paper "LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term… adityalab/camul — We require you to have anaconda or miniconda installed. Run the script ./scripts/setup.sh to setup the virtual…
Lag-llama is a probabilistic machine learning foundation model designed for time series forecasting. It generates predictive distributions and uncertainty bounds for sequential data across arbitrary frequencies by leveraging pre-trained foundational weights. The system supports zero-shot transfer inference, allowing it to predict future values on entirely new and unseen datasets without requiring prior retraining. It achieves this by combining generalized representations from foundational training with adjustable context lengths, where historical context lengths and lagged feature values feed
This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque
