Updates to time series library

Author: gabydreckschmidt

lazy-timeseries-evaluation.md

@@ -106,3 +106,7 @@ def harsh(a):
     else:
         return 0
 ```
+
+## Learn more
+
+To use the `tspy` Python SDK, see the [`tspy` Python SDK documentation](https://ibm-cloud.github.io/tspy-docs/).

time-reference-system.md

@@ -227,3 +227,7 @@ This returns:
 # average = (1+2+4)/3 = 2.33
 [(0,2.33)]
 ```
+
+## Learn more
+
+To use the `tspy` Python SDK, see the [`tspy` Python SDK documentation](https://ibm-cloud.github.io/tspy-docs/).

time-series-functions.md

@@ -311,3 +311,7 @@ timestamp      value  lower_bound  upper_bound     error
 The time series library is tightly integrated with Apache Spark. By using new data types in Spark Catalyst, you are able to perform time series SQL operations that scale out horizontally using Apache Spark. This enables you to easily use time series extensions in {{site.data.keyword.iae_full_notm}} or in solutions that include {{site.data.keyword.iae_full_notm}} functionality like the {{site.data.keyword.DSX_short}} Spark environments.
 
 SQL extensions cover most aspects of the time series functions, including segmentation, transformations, reducers, forecasting, and I/O. See [Analyzing time series data](https://cloud.ibm.com/docs/sql-query?topic=sql-query-ts_intro).
+
+## Learn more
+
+To use the `tspy` Python SDK, see the [`tspy` Python SDK documentation](https://ibm-cloud.github.io/tspy-docs/).

time-series-key-functionality.md

@@ -100,3 +100,7 @@ The following data types are supported in a time series:
 ## Time series functions
 
 You can use different functions in the provided time series packages to analyze time series data to extract meaningful information with which to create models that can be used to predict new values based on previously observed values. See [Time series functions](/docs/AnalyticsEngine?topic=AnalyticsEngine-time-series-functions).
+
+## Learn more
+
+To use the `tspy` Python SDK, see the [`tspy` Python SDK documentation](https://ibm-cloud.github.io/tspy-docs/).

using-time-series-lib.md

@@ -273,3 +273,4 @@ max:7.0
 - [Time series functions](/docs/AnalyticsEngine?topic=AnalyticsEngine-time-series-functions)
 - [Time series lazy evaluation](/docs/AnalyticsEngine?topic=AnalyticsEngine-lazy-evaluation)
 - [Using time reference system](/docs/AnalyticsEngine?topic=AnalyticsEngine-time-reference-system)
+- [`tspy` Python SDK documentation](https://ibm-cloud.github.io/tspy-docs/)