Awesome
PrimeNet : Pre-Training for Irregular Multivariate Time Series
This is the official PyTorch implementation of the AAAI 2023 paper PrimeNet: Pre-Training for Irregular Multivariate Time Series.
Quick Start
git clone https://github.com/ranakroychowdhury/PrimeNet.git
Datasets
Activity
Run preprocess/download_activity.sh to download and place the data under the proper directory.
Run preprocess/preprocess_activity.py to preprocess the data.
MIMIC-III
Follow the instruction from interp-net to download and preprocess the data.
Appliances Energy
Download the dataset.
Run preprocess/preprocess_ae.py to preprocess the data.
PhysioNet
Run preprocess/preprocess_physionet.py to download and preprocess the data.
The data directory structure should be the same as that in data.zip. Extract data.zip to run experiments with a sample toy dataset.
data/
pretrain/
X_train.pt
X_val.ptfinetune/
X_train.pt
y_train.pt
X_val.pt
y_val.pt
X_test.pt
y_test.pt
Pre-training
Run pretrain.sh to run pretraining experiments on a dataset. The pretrained model is saved under ./models/ and the pretraining results are stored under ./results/. The arguments for pretraining are explained in pretrain.py.
sh pretrain.sh
Fine-tuning and Evaluation
Run finetune.sh to run finetuning experiments on a dataset. The pretrained model saved during the pretraining experiment under ./models/ is used for finetuning. The name of the pretrained model to use is added as an argument in the finetuning command. The finetuning results are stored under ./results/. The arguments for finetuning are explained in finetune.py.
sh finetune.sh