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---
license: cc-by-4.0
task_categories:
- time-series-forecasting
pretty_name: cloud
size_categories:
- 100M<n<1B
---
# Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
[Paper](https://arxiv.org/abs/2310.05063) | [Code](https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops)
Datasets accompanying the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain".
```python
from datasets import load_dataset
dataset = load_dataset('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')
```
### azure_vm_traces_2017
```python
DatasetDict({
train_test: Dataset({
features: ['start', 'target', 'item_id', 'feat_static_cat', 'feat_static_real', 'past_feat_dynamic_real'],
num_rows: 17568
})
pretrain: Dataset({
features: ['start', 'target', 'item_id', 'feat_static_cat', 'feat_static_real', 'past_feat_dynamic_real'],
num_rows: 159472
})
})
```
### borg_cluster_data_2011
```python
DatasetDict({
train_test: Dataset({
features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
num_rows: 11117
})
pretrain: Dataset({
features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
num_rows: 143386
})
})
```
### alibaba_cluster_trace_2018
```python
DatasetDict({
train_test: Dataset({
features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
num_rows: 6048
})
pretrain: Dataset({
features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
num_rows: 58409
})
})
```
## Acknowledgements
The datasets were processed from the following original sources. Please cite the original sources if you use the datasets.
* Azure VM Traces 2017
* Bianchini. Resource central: Understanding and predicting workloads for improved resource
management in large cloud platforms. In Proceedings of the 26th Symposium on Operating Systems
Principles, pp. 153–167, 2017.
* https://github.com/Azure/AzurePublicDataset
* Borg Cluster Data 2011
* John Wilkes. More Google cluster data. Google research blog, November 2011. Posted at http:
//googleresearch.blogspot.com/2011/11/more-google-cluster-data.html.
* https://github.com/google/cluster-data
* Alibaba Cluster Trace 2018
* Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, and Yungang Bao. Who
limits the resource efficiency of my datacenter: An analysis of alibaba datacenter traces. In
Proceedings of the International Symposium on Quality of Service, pp. 1–10, 2019.
* https://github.com/alibaba/clusterdata
## Citation
```
@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}
}
```
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