Even for short list (15 elem) of random time segments of DateObjects, Accumulate consumes unexpectedly long times. But, when time segments are not random, or are Accumulated as a Quantity, prior adding to DateObject (conversion to DateObject), there are no such long computation long times. For list of length few dozens computation time approaches infinity. Eg.

Delta times before accumulation:

dtimqr = Quantity[IntegerPart[RandomVariate[GammaDistribution[9, 0.1], 15]*86400], "Seconds"];
dtimq = Quantity[Table[135000, 15], "Seconds"];

Accumulation as a DateObjects:

{Timing[ftimr = Accumulate@({FromUnixTime[0]}~Join~dtimqr);], 
 Timing[ftim = Accumulate@({FromUnixTime[0]}~Join~dtimq);]}
(* {{14.0625, Null}, {0.0625, Null}} *)

{Timing[ftimrP=FromUnixTime[0]+Accumulate@({Quantity[0, "Seconds"]}~Join~dtimqr);],
 Timing[ftimP=FromUnixTime[0]+Accumulate@({Quantity[0, "Seconds"]}~Join~dtimq);]}
(* {{0.0625, Null}, {0.046875, Null}} *)

Is there reasonable explanation for such a long computation time when Accumulating lists with random dateObjects, as short as 15 elements. ?

  • 2
    $\begingroup$ There's a lot of overhead involved in carrying around and manipulating DateObject[]s. I would suggest keeping things in UnixTime[] or AbsoluteTime[], and convert to explicit dates only when you need to display them. $\endgroup$ Mar 12, 2018 at 19:05
  • $\begingroup$ @J.M. but why when DateObjects are random, only? I have few thousands objects from TimeSeries data, and if this is the case, this will complicate my code a lot. $\endgroup$
    – Dragutin
    Mar 12, 2018 at 19:23

1 Answer 1


You should use Date Operations functions as found in the Date & Time guide to operate on DateObjects. For example, you may use DatePlus with FoldList.

dtimqr = Quantity[
   IntegerPart[RandomVariate[GammaDistribution[9, 0.1], 15]*86400], 

Compare DatePlus

AbsoluteTiming[FoldList[DatePlus[##] &, FromUnixTime@0, dtimqr];]
{0.00311076, Null}

to Accumulate

{9.56173, Null}

Hope this helps.


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