fix

README.md

@@ -8,16 +8,19 @@ size_categories:
 ---
 
 # 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".
 
+## Quick Start
 ```python
 from datasets import load_dataset
 
 dataset = load_dataset('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')
 ```
 
+## Available Datasets
 ### azure_vm_traces_2017
 ```python
 DatasetDict({
@@ -60,31 +63,57 @@ DatasetDict({
 })
 ```
 
----
+## Dataset Config
 ```python
 from datasets import load_dataset_builder
 
 config = load_dataset_builder('Salesforce/cloudops_tsf', 'azure_vm_traces_2017').config
-config.
+print(config)
+
+CloudOpsTSFConfig(
+    name='alibaba_cluster_trace_2018',
+    version=1.0.0,
+    data_dir=None,
+    data_files=None,
+    description='',
+    prediction_length=48,
+    freq='5T',
+    stride=48,
+    univariate=False,
+    multivariate=True,
+    optional_fields=('feat_static_cat', 'past_feat_dynamic_real'),
+    rolling_evaluations=12,
+    test_split_date=Period('2018-01-08 11:55', '5T'),
+    _feat_static_cat_cardinalities={
+        'pretrain': (
+            ('container_id', 64457),
+            ('app_du',9484)),
+        'train_test': (
+            ('container_id', 6048),
+            ('app_du', 1292)
+        )
+    },
+    target_dim=2,
+    feat_static_real_dim=0,
+    past_feat_dynamic_real_dim=6
+)
 ```
+```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.
 * 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.
+  * 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.
+  * 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.
+  * 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