Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Awesome-TimeSeries-SpatioTemporal-LM-LLM
A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.
https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM
- **Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook**
- \[paper\
- \[paper\ - LLM)
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\ - DI-ML/NeurIPS2023-One-Fits-All)
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\ - code\]](https://github.com/ant-research/fin_domain_llm)
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\ - code\]](https://github.com/iLampard/ep_llm)
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\ - harvard/TFC-pretraining)
- \[paper\
- \[paper\
- \[paper\ - Reprogramming)
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[link\
- \[link\
- \[link\
- \[link\
- \link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[paper\
- \[paper\
- \[paper\ - Edgerunners/LLM-Adapters)
- \[paper\
- \[paper\ - Qiu/Awesome-Healthcare-Foundation-Models)
- \[paper\ - Foundation/FinGPT)
- \[paper\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[link\
- \[paper\
- \[paper\
- \[paper\
- \[paper\ - SSL4TS)
- \[paper\ - GNN4TS)
- \[paper\ - series-transformers-review)
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\ - lab/time-series-ptms)
- \[paper\ - SSL4TS)
- \[paper\ - GNN4TS)
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
- \[paper\
Programming Languages
Keywords
dataset
5
large-language-models
3
time-series
3
benchmark
1
deep-learning
1
weather-forecast
1
prices
1
stock-prediction
1
tweets
1
cvpr2017
1
snuvl
1
tgif-qa
1
benchmarking
1
machine-learning-algorithms
1
stochastic-processes
1
temporal-data
1
electricity-transformer-dataset
1
forecasting
1
vqa-dataset
1
vqa
1
video-reasoning
1
video-qa
1
traffic-events
1
paper
1
multimodal-deep-learning
1
multimodal
1
cvpr2021
1
cvpr
1
annotations
1
traffic-flow-forecasting
1
graph
1
attention
1
aaai
1
visual-instruction-tuning
1
visual-in-context-learning
1
visual-chain-of-thought
1
multimodal-large-language-models
1
multimodal-instruction-tuning
1
multimodal-in-context-learning
1
multimodal-chain-of-thought
1
multi-modality
1
large-vision-language-models
1
large-vision-language-model
1
instruction-tuning
1
instruction-following
1
in-context-learning
1
chain-of-thought
1
vision-transformer
1
transformer-models
1
foundation-models
1