Multivariate Time Series Transformer Framework
This code corresponds to the paper: George Zerveas et al. A Transformer-based Framework for Multivariate Time Series Representation Learning, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), August 14-18, 2021. ArXiV version: https://arxiv.org/abs/2010.02803
If you find this code or any of the ideas in the paper useful, please consider citing:
@inproceedings{10.1145/3447548.3467401,
author = {Zerveas, George and Jayaraman, Srideepika and Patel, Dhaval and Bhamidipaty, Anuradha and Eickhoff, Carsten},
title = {A Transformer-Based Framework for Multivariate Time Series Representation Learning},
year = {2021},
isbn = {9781450383325},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3447548.3467401},
doi = {10.1145/3447548.3467401},
booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining},
pages = {2114–2124},
numpages = {11},
keywords = {regression, framework, multivariate time series, classification, transformer, deep learning, self-supervised learning, unsupervised learning, imputation},
location = {Virtual Event, Singapore},
series = {KDD '21}
}
Setup
Instructions refer to Unix-based systems (e.g. Linux, MacOS).
cd mvts_transformer/
Inside an already existing root directory, each experiment will create a time-stamped output directory, which contains
model checkpoints, performance metrics per epoch, predictions per sample, the experiment configuration, log files etc.
The following commands assume that you have created a new root directory inside the project directory like this:
mkdir experiments.
[We recommend creating and activating a conda or other Python virtual environment (e.g. virtualenv) to
install packages and avoid conficting package requirements; otherwise, to run pip, the flag --user or sudo privileges will be necessary.]
pip install -r requirements.txt
[Note: Because sometimes newer versions of packages break backward compatibility with previous versions or other packages, instead or requirements.txt you can use failsafe_requirements.txt to use the versions which have been tested to work with this codebase.]
Download dataset files and place them in separate directories, one for regression and one for classification.
Classification: http://www.timeseriesclassification.com/Downloads/Archives/Multivariate2018_ts.zip
Regression: https://zenodo.org/record/3902651#.YB5P0OpOm3s
Example commands
To see all command options with explanations, run: python src/main.py --help
You should replace $1 below with the name of the desired dataset.
The commands shown here specify configurations intended for BeijingPM25Quality for regression and SpokenArabicDigits for classification.
[To obtain best performance for other datasets, use the hyperparameters as given in the Supplementary Material of the paper.
Appropriate downsampling with the option --subsample_factor can be often used on datasets with longer time series to speedup training, without significant
performance degradation.]
The configurations as shown below will evaluate the model on the TEST set periodically during training, and at the end of training.
Besides the console output and the logfile output.log, you can monitor the evolution of performance (after installing tensorboard: pip install tensorboard) with:
tensorboard dev upload --name my_exp --logdir path/to/output_dirTrain models from scratch
Regression
(Note: the loss reported for regression is the Mean Square Error, i.e. without the Root)
python src/main.py --output_dir path/to/experiments --comment "regression from Scratch" --name $1_fromScratch_Regression --records_file Regression_records.xls --data_dir path/to/Datasets/Regression/$1/ --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --optimizer RAdam --pos_encoding learnable --task regressionClassification
python src/main.py --output_dir experiments --comment "classification from Scratch" --name $1_fromScratch --records_file Classification_records.xls --data_dir path/to/Datasets/Classification/$1/ --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable --task classification --key_metric accuracyPre-train models (unsupervised learning through input masking)
Can be used for any downstream task, e.g. regression, classification, imputation.
Make sure that the network architecture parameters of the pretrained model match the parameters of the desired fine-tuned model (e.g. use --d_model 64 for SpokenArabicDigits).
python src/main.py --output_dir experiments --comment "pretraining through imputation" --name $1_pretrained --records_file Imputation_records.xls --data_dir /path/to/$1/ --data_class tsra --pattern TRAIN --val_ratio 0.2 --epochs 700 --lr 0.001 --optimizer RAdam --batch_size 32 --pos_encoding learnable --d_model 128Fine-tune pretrained models
Make sure that network architecture parameters (e.g. d_model) used to fine-tune a model match the pretrained model.
Regression
python src/main.py --output_dir experiments --comment "finetune for regression" --name BeijingPM25Quality_finetuned --records_file Regression_records.xls --data_dir /path/to/Datasets/Regression/BeijingPM25Quality/ --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 200 --lr 0.001 --optimizer RAdam --pos_encoding learnable --d_model 128 --load_model path/to/BeijingPM25Quality_pretrained/checkpoints/model_best.pth --task regression --change_output --batch_size 128Classification
python src/main.py --output_dir experiments --comment "finetune for classification" --name SpokenArabicDigits_finetuned --records_file Classification_records.xls --data_dir /path/to/Datasets/Classification/SpokenArabicDigits/ --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --optimizer RAdam --batch_size 128 --pos_encoding learnable --d_model 64 --load_model path/to/SpokenArabicDigits_pretrained/checkpoints/model_best.pth --task classification --change_output --key_metric accuracy