The collection of resources about LLM for Time series tasks

(ICLR'25) PaPaGei: Open Foundation Models for Optical Physiological Signals

Repository for the paper 'Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care'.

The code, data, and models for "Teach Multimodal LLMs to Comprehend Electrocardiographic Images".

A curated list of resources for using LLMs to develop more competitive grant applications.

pyPPG v01

Finetune ALL LLMs with ALL Adapeters on ALL Platforms!

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

Pretraining codes for ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text

A Framework of Small-scale Large Multimodal Models

This is the official code of our Paper "DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecasting"

Retrieval Augmented Generation (RAG) chatbot powered by Weaviate

MOMENT: A Family of Open Time-series Foundation Models

[ICLR 2024 spotlight] Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

Scalable and user friendly neural 🧠 forecasting algorithms.

Unified Training of Universal Time Series Forecasting Transformers

A unified multi-task time series model.

A customizable pipeline for data extraction from MIMIC-IV.

A python package for sampling methods and flow models

Official implementation for 'Class-Balancing Diffusion Models'

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

Code of ICLR2023 paper "TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene Understanding" and ECCV2022 paper "Inverted Pyramid Multi-task Transformer for Dense Scene Understanding"

A Library for Advanced Deep Time Series Models.