Update how-to-create-register-datasets.md

added timeseries trait

Author: lostmygithubaccount

articles/machine-learning/service/how-to-create-register-datasets.md

@@ -42,7 +42,7 @@ To create and work with datasets, you need:
 ## Dataset Types
 
 Datasets are categorized into various types based on how users consume them in training. List of Dataset types:
-* [TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas DataFrame. A `TabularDataset` object can be created from csv, tsv, parquet files, SQL query results etc. For a complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference).
+* [TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas DataFrame. A `TabularDataset` object can be created from csv, tsv, parquet files, SQL query results etc. For a complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). A timestamp can be specified from a column in the data or the path pattern data is stored in to enable a timeseries trait, which allows for easy and efficient filtering by time. 
 * [FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.file_dataset.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. This provides you with the ability to download or mount the files to your compute. The files can be of any format, which enables a wider range of machine learning scenarios including deep learning.
 
 To find out more about upcoming API changes, see [here](https://aka.ms/tabular-dataset).
@@ -104,6 +104,26 @@ titanic_ds.take(3).to_pandas_dataframe()
 1|2|1|1|Cumings, Mrs. John Bradley (Florence Briggs Th...|female|38.0|1|0|PC 17599|71.2833|C85|C
 2|3|1|3|Heikkinen, Miss. Laina|female|26.0|0|0|STON/O2. 3101282|7.9250||S
 
+
+Use the `with_timestamp_columns()` method on `TabularDataset` class to enable easy and efficient filtering by time. More examples and details can be found [here](http://aka.ms/azureml-tsd-notebook). 
+
+```Python
+# create a TabularDataset with timeseries trait
+datastore_paths = [(datastore, 'weather/*/*/*/data.parquet')]
+
+# get a coarse timestamp column from the path pattern
+dataset = Dataset.Tabular.from_parquet_files(path=datastore_path, partition_format='weather/{coarse_time:yyy/MM/dd}/data.parquet')
+
+# set coarse timestamp to the virtual column created, and fine grain timestamp from a column in the data
+dataset = dataset.with_timestamp_columns(fine_grain_timestamp='datetime', coarse_grain_timestamp='coarse_time')
+
+# filter with timeseries trait specific methods 
+data_slice = dataset.time_before(datetime(2019, 1, 1))
+data_slice = dataset.time_after(datetime(2019, 1, 1))
+data_slice = dataset.time_between(datetime(2019, 1, 1), datetime(2019, 2, 1)) 
+data_slice = dataset.time_recent(timedelta(weeks=1, days=1))                  
+```
+
 #### Workspace landing page 
 
 Sign in to the [workspace landing page](https://ml.azure.com) to create a dataset via the web experience. Currently, the workspace landing page only supports the creation of TabularDatasets.