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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
TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and anomaly detection. It functions as a pretrained model for predicting future values in univariate time series data, eliminating the need for manual training from scratch. The project includes a framework for adapting pretrained weights to specific datasets using low-rank adaptation to improve accuracy. It also provides specialized capabilities for integrating time-series predictions as tools within autonomous AI agent architectures and automated workflows. The system supports
Voice2Series: Reprogramming / Prompting Acoustic Models for Time Series Classification
Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin, "One Fits All: Power General Time Series Analysis by Pretrained LM,", NeurIPS, 2023. paper
The main features of adityalab/time-mmd are: Time Series Foundation Models.
Projects with overlapping indexed features include: time-series-foundation-models/lag-llama — Lag-llama is a probabilistic machine learning foundation model designed for time series forecasting. It generates… google-research/timesfm — TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and… huckiyang/voice2series-reprogramming — Voice2Series: Reprogramming / Prompting Acoustic Models for Time Series Classification. kimmeen/time-llm — (ICLR'24) Time-LLM: Time Series Forecasting by Reprogramming Large Language Models. mims-harvard/tfc-pretraining — This repository contains eight processed datasets and the codes of developed TF-C pretraining model (along with… damo-di-ml/neurips2023-one-fits-all — Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin, "One Fits All: Power General Time Series Analysis by Pretrained…