Profiling the Missing Value Structure of Your Timeseries Data

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Usually we use a line-chart to "look" at our timeseries data. Sure, this is definitely a good way to visualize trends, seasons, cycles, outliers in your data and it allows you to decide about the modeling strategy that you would like to apply.
From a data quality point of view, however, we would in addition like to get a different look at our data. You would like to see whether there are missing values or zero values in the time series, or whether there are time periods where you have a large cumulation of missing values or zero values. You would also like to get information about the length of your timeseries and whether there is a sufficient data history to run timeseries forecasting models.
Watch this video to see how you can use a powerful SAS Macro or SAS Visual Analytics for that task.

This session focuses on "Data Quality for Analytics" and is part of the "Data Preparation for Data Science" webinar: Assemble-the-data | Data-Quality-for-Analytics | Feature-Generation

0:00 Introduction and business question
1:28 Using the %PROFILE_TS_MV macro to get a detailed picture
13:36 Using SAS Visual Analytics to profile the missing value structure
19:58 Summary and Closing

Links to related content
Replace MISSING VALUES in TIMESERIES DATA using PROC EXPAND and PROC TIMESERIES:
SGF-Paper: Want an Early Picture of the Data Quality Status of Your Analysis Data? SAS® Visual Analytics Shows You How

SAS Press Books
Data Quality for Analytics Using SAS
Data Preparation for Analytics Using SAS

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