BEGIN:VCALENDAR CALSCALE:GREGORIAN VERSION:2.0 PRODID:-//Events Calendar//iCal4j 1.0//EN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20260306T231828Z TZURL:https://www.tzurl.org/zoneinfo/America/New_York X-LIC-LOCATION:America/New_York X-PROLEPTIC-TZNAME:LMT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-045602 TZOFFSETTO:-0500 DTSTART:18831118T120358 END:STANDARD BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19180331T020000 RRULE:FREQ=YEARLY;UNTIL=19200328T070000Z;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19181027T020000 RRULE:FREQ=YEARLY;UNTIL=19201031T060000Z;BYMONTH=10;BYDAY=-1SU END:STANDARD BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19210424T020000 RRULE:FREQ=YEARLY;UNTIL=19410427T070000Z;BYMONTH=4;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19210925T020000 RRULE:FREQ=YEARLY;UNTIL=19410928T060000Z;BYMONTH=9;BYDAY=-1SU END:STANDARD BEGIN:DAYLIGHT TZNAME:EWT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19420209T020000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EPT TZOFFSETFROM:-0400 TZOFFSETTO:-0400 DTSTART:19450814T190000 END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19450930T020000 END:STANDARD BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19460428T020000 RRULE:FREQ=YEARLY;UNTIL=19730429T070000Z;BYMONTH=4;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19460929T020000 RRULE:FREQ=YEARLY;UNTIL=19540926T060000Z;BYMONTH=9;BYDAY=-1SU END:STANDARD BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19551030T020000 RRULE:FREQ=YEARLY;UNTIL=20061029T060000Z;BYMONTH=10;BYDAY=-1SU END:STANDARD BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19740106T020000 RDATE:19750223T020000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19760425T020000 RRULE:FREQ=YEARLY;UNTIL=19860427T070000Z;BYMONTH=4;BYDAY=-1SU END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19870405T020000 RRULE:FREQ=YEARLY;UNTIL=20060402T070000Z;BYMONTH=4;BYDAY=1SU END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260419T050231Z DTSTART;TZID=America/New_York:20240508T120000 DTEND;TZID=America/New_York:20240508T130000 SUMMARY:BASUG: History Carried Forward\, Future Carried Back: Mixing Time S eries of Differing Frequencies LAST-MODIFIED:20260419T050231Z LOCATION:https://us02web.zoom.us/webinar/register/WN_7fc44Ny9TUuzsxwCAwOLfQ #/registration TZID:America/New_York UID:message-600 DESCRIPTION:Click the link below to go to the event page:\nhttps://communit ies.sas.com/t5/Upcoming-Events/BASUG-History-Carried-Forward-Future-Carrie d-Back-Mixing-Time/ec-p/925566#M600\n\nMany programming tasks require merg ing time series of varying frequency. For instance you might have three da tasets (YEAR\, QTR\, and MONTH) of data\, each with eponymous frequency an d sorted by common id and date variables. Producing a monthly file with th e most recent quarterly and yearly data is a hierarchical last-observation -carried-forward (LOCF) task. Or you may have three irregular times series (ADMISSIONS\, SERVICES\, TESTRESULTS)\, in which you want to capture the latest data from each source at every date encountered (event-based LOCF). These are tasks often left poorly optimized by most SQL-based languages\, in which row order is ignored in the interests of optimizing table manipu lation.   This presentation shows how to use conditional SET statements i n the SAS® DATA step to update specific portions of the program data vecto r (i.e. the YEAR variables or the QTR variables) to carry forward low freq uency data to multiple subsequent high frequency records. A similar approa ch works just as well for carrying forward data from irregular time series . We’ll also show how to use “sentinel variables” as a means of controllin g the maximum time-span data is carried forward\, i.e. how to remove histo rical data that has become “stale.” Finally\, we will demonstrate how to m odify these techniques to carry future observations backward\, without re- sorting data.   Presented by Mark Keintz Mark Keintz has been using SAS® s ince it was documented in one book. His interests are largely in developme nt of applications for financial research and education\, addressed in sev eral presentations at SAS Global Forums and various regional SAS user grou ps. Mark's primary SAS expertise is in DATA step programming\, hash progra mming techniques\, efficient use of large data sets\, and macro programmin g.   REGISTER for this virtual event today. X-ALT-DESC;FMTTYPE=text/html:

Click the link below to go to the event pag e:


ht tps://communities.sas.com/t5/Upcoming-Events/BASUG-History-Carried-Forward -Future-Carried-Back-Mixing-Time/ec-p/925566#M600

Many programming tasks require merging time series of varying frequency. For i nstance you might have three datasets (YEAR\, QTR\, and MONTH) of data\, e ach with eponymous frequency and sorted by common id and date variables. P roducing a monthly file with the most recent quarterly and yearly data is a hierarchical last-observation-carried-forward (LOCF) task. Or you may ha ve three irregular times series (ADMISSIONS\, SERVICES\, TESTRESULTS)\, in which you want to capture the latest data from each source at every date encountered (event-based LOCF). These are tasks often left poorly optimize d by most SQL-based languages\, in which row order is ignored in the inter ests of optimizing table manipulation.

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This presentation shows how to use conditional SET statements in the SA S® DATA step to update specific portions of the program data vector (i.e. the YEAR variables or the QTR variables) to carry forward low frequency da ta to multiple subsequent high frequency records. A similar approach works just as well for carrying forward data from irregular time series. We’ll also show how to use “sentinel variables” as a means of controlling the ma ximum time-span data is carried forward\, i.e. how to remove historical da ta that has become “stale.” Finally\, we will demonstrate how to modify th ese techniques to carry future observations backward\, without re-sorting data.

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Presented by \;Mark Keint z

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Mark Keintz has been using SAS® since it was documented in one book. \;His interests a re largely in development of applications for financial research and educa tion\, addressed in several presentations at SAS Global Forums and various regional SAS user groups. \;Mark's primary SAS expertise is in DATA s tep programming\, hash programming techniques\, efficient use of large dat a sets\, and macro programming.

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REGISTER for this virtual event today.

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