Datasets:

ArXiv:
License:
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ImportError
Message:      To be able to use Salesforce/cloudops_tsf, you need to install the following dependency: gluonts.
Please install it using 'pip install gluonts' for instance.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory
                  return HubDatasetModuleFactoryWithScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module
                  local_imports = _download_additional_modules(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules
                  raise ImportError(
              ImportError: To be able to use Salesforce/cloudops_tsf, you need to install the following dependency: gluonts.
              Please install it using 'pip install gluonts' for instance.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

Paper | Code

Datasets accompanying the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain".

Quick Start

pip install datasets==2.12.0 fsspec==2023.5.0

azure_vm_traces_2017

from datasets import load_dataset

dataset = load_dataset('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')
print(dataset)

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

dataset = load_dataset('Salesforce/cloudops_tsf', 'borg_cluster_data_2011')
print(dataset)

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

dataset = load_dataset('Salesforce/cloudops_tsf', 'alibaba_cluster_trace_2018')
print(dataset)

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
    })
})

Dataset Config

from datasets import load_dataset_builder

config = load_dataset_builder('Salesforce/cloudops_tsf', 'azure_vm_traces_2017').config
print(config)

CloudOpsTSFConfig(
    name='azure_vm_traces_2017',
    version=1.0.0,
    data_dir=None,
    data_files=None,
    description='',
    prediction_length=48,
    freq='5T',
    stride=48,
    univariate=True,
    multivariate=False,
    optional_fields=(
        'feat_static_cat',
        'feat_static_real',
        'past_feat_dynamic_real'
    ),
    rolling_evaluations=12,
    test_split_date=Period('2016-12-13 15:55', '5T'),
    _feat_static_cat_cardinalities={
        'pretrain': (
            ('vm_id', 177040),
            ('subscription_id', 5514),
            ('deployment_id', 15208),
            ('vm_category', 3)
        ),
        'train_test': (
            ('vm_id', 17568),
            ('subscription_id', 2713),
            ('deployment_id', 3255),
            ('vm_category', 3)
        )
    },
    target_dim=1,
    feat_static_real_dim=3,
    past_feat_dynamic_real_dim=2
)

test_split_date is provided to achieve the same train-test split as given in the paper. This is essentially the date/time of rolling_evaluations * prediction_length time steps before the last time step in the dataset. Note that the pre-training dataset includes the test region, and thus should also be filtered before usage.

Acknowledgements

The datasets were processed from the following original sources. Please cite the original sources if you use the datasets.

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}
}
Downloads last month
379