From adaad766b3c7115cca1e77e5e72b434659511951 Mon Sep 17 00:00:00 2001 From: YunhaoZhang-Mars Date: Sun, 9 Jun 2024 17:04:45 +0800 Subject: [PATCH] update paper link and citation --- readme.md | 35 ++++++++++++++++++++++++----------- 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/readme.md b/readme.md index 2907326..2a47aee 100644 --- a/readme.md +++ b/readme.md @@ -1,6 +1,6 @@ # UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis (ICML 2024) -This is the origin Pytorch implementation of “UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis （ICML L2024)” +This is the origin Pytorch implementation of “[UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis (ICML 2024)](https://openreview.net/pdf?id=aR3uxWlZhX)” ## Workflow UP2ME is a general-purpose framework for Multivariate Time Series Analysis. It conducts taskagnostic pre-training when downstream tasks are unspecified. Once the task and setting (e.g. forecasting length) are determined, it gives sensible solutions with frozen pre-trained parameters. Further accuracy is achieved through multivariate fine-tuning. @@ -50,15 +50,17 @@ Graph Transformer layer. The Graph Transformer layer takes the constructed depen 2. Download the datasets from [UP2ME-datasets](https://drive.google.com/file/d/1oLYcQa7NJcMDSP_rYSkP5hQHzXL2rpZM/view?usp=drive_link) and unzip it into the folder `datasets` in the root folder. The struture should be like: - - datasets - - ETT - - ETTm1.csv - - ...... same for csv format datasets: weather, ECL(Electricity) and traffic - - SMD - - SMD_train.npy - - SMD_test.npy - - SMD_test_label.npy - - ...... same for npy format datasets: PSM, SWaT and NIPS_Water(GECCO) + ``` + datasets + ├── ETT + │ └── ETTm1.csv + ├── same for csv format datasets: weather, ECL(Electricity) and traffic + ├── SMD + │ ├── SMD_train.npy + │ ├── SMD_test.npy + │ └── SMD_test_label.npy + └── same for npy format datasets: PSM, SWaT and NIPS_Water(GECCO) + ``` @@ -66,10 +68,21 @@ Graph Transformer layer. The Graph Transformer layer takes the constructed depen ``` bash scripts/forecast_scripts/ETTm1.sh ``` - the immediate reaction(UP2ME(IR)) and fine-tuning(UP2ME(FT)) modes will be tested on 4 different forecasting lengths (96, 192, 336, 720) and results will be saved in a new folder `./forecast_result`. + the immediate reaction(UP2ME(IR)) and fine-tuning(UP2ME(FT)) modes will be tested on 4 different forecasting lengths (96, 192, 336, 720) and results will be saved in a new folder `./forecast_results`. 4. To reproduce results for all 3 tasks on all 8 datasets, run other scripts in `./scripts`. +## Citation +If you find this repository useful in your research, please cite: +``` +@inproceedings{zhang2024upme, +title={{UP}2{ME}: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis}, +author={Yunhao Zhang and Minghao Liu and Shengyang Zhou and Junchi Yan}, +booktitle={International Conference on Machine Learning (ICML)}, +year={2024} +} +``` + ## Acknowledgement We appreciate the following works for their valuable code and data: