Official code repository for the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain". Check out our paper for more details. Accompanying datasets can be found here.
We use Hydra for config management.
Run the pre-training script:
python -m pretraining.pretrain_exp backbone=BACKBONE size=SIZE ++data.dataset_name=DATASET
- where the options for
BACKBONE
,SIZE
options can be found inconf/backbone
andconf/size
respectively. DATASET
is one ofazure_vm_traces_2017
,borg_cluster_data_2011
, oralibaba_cluster_trace_2018
.- see
confg/pretrain.yaml
for more details on the options. - training logs and checkpoints will be saved in
outputs/
Run the forecast script:
python -m pretraining.forecast_exp backbone=BACKBONE forecast=FORECAST size=SIZE ++data.dataset_name=DATASET
- where the options for
BACKBONE
,FORECAST
,SIZE
options can be found inconf/backbone
,conf/forecast
, andconf/size
respectively. DATASET
is one ofazure_vm_traces_2017
,borg_cluster_data_2011
, oralibaba_cluster_trace_2018
.- see
confg/forecast.yaml
for more details on the options. - training logs and checkpoints will be saved in
outputs/
If you find the paper or the source code useful to your projects, please cite the following bibtex:
@article{woo2023pushing, title={Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain}, author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Sahoo, Doyen}, journal={arXiv preprint arXiv:2310.05063}, year={2023} }