Mathematica Asked on May 7, 2021
Bug introduced in 12.0 or earlier and persisting through 12.2 or later
It seems we’ve discovered a bug! The following is a summary of the problem and possible workarounds. Scroll down to ORIGINAL POST to see the original statement of the issue.
If a List
, TimeSeries
, etc. of Around
objects contains any identical elements, Accumulate
produces propagated uncertainties that are too large. FoldList[Plus, 0, list]
and FoldList[Plus]@list
are valid alternatives that do not present this issue for Lists
, but these do not work with TimeSeries
! Finding something which does work with TimeSeries
is still a work in progress.
If a List
, TimeSeries
, etc. of Around
objects contains consecutive identical elements, Differences
produces 0.
, which does not have an associated uncertainty. MovingMap[Subtract[#[[2]], #[[1]]] &, list, 1]
and MovingMap[Subtract[#[[2]], #[[1]]] &, #, 1] &@list
are valid alternatives which do not present this issue. And they work with TimeSeries
!
Analysis of these and other functions can be found here.
For now, my best solution is to create a symbolic TimeSeries
to manipulate with Accumulate
, Differences
, Ratios
, etc., and then use ReplaceAll
to insert the Around
data in the final step. I’m not using AroundReplace
because that function took over ten minutes to process a single TimeSeries
! But, one must be careful when using ReplaceAll
in lieu of AroundReplace
. According to the documentation,
[
ReplaceAll
andAroundReplace
] coincide if each replaced variable appears once in the original
expression.
One must therefore perform calculations in a manner that ensures this is the case.
————————————ORIGINAL POST————————————
I am working with several TimeSeries
containing Around
objects. I find that the propagated uncertainties for numerical values are larger than I expect them to be. In contrast, the propagated uncertainties for symbolic values are exactly what I expect them to be.
This is also true for lists of Around
objects, so I will use List
to demonstrate. Note that the original data values are all integers greater than or equal to zero (0 ≤ n[i] ∈ ℤ
) and their original uncertainties equal their square roots (δn[i] = √n[i]
)
First, generate a list of symbolic data:
symbolicData = Table[Around[n[i], Sqrt[n[i]]], {i, 10}]
Then, Accumulate
these data:
symbolicTotals = Accumulate@symbolicData
The uncertainty of the last value is the square root of that value:
{symbolicTotals[[-1]]["Uncertainty"], Sqrt@symbolicTotals[[-1]]["Value"]}
Given my understanding of error propagation, this is correct.
Now, generate numerical data:
numericalData = Table[Around[#, Sqrt[#]] &@RandomInteger[10], {i, 10}]
Acculumate
these data:
numericalTotals = Accumulate@numericalData
Compare the last value’s uncertainty and square root:
{numericalTotals[[-1]]["Uncertainty"], Sqrt@numericalTotals[[-1]]["Value"]}
The uncertainty is larger than the square root. This happens for any long list of numbers.
Why? And how can I fix it? (Assuming there’s an actual problem here, and not simply a lack of knowledge on my end.)
My guess is that this discrepancy is a consequence of the fact that
When Around is used in computations, uncertainties are by default propagated using a first-order series approximation, assuming no correlations.
What exactly is being approximated here, and if Around
always does this, why doesn’t it affect symbolic values?
I understand that AroundReplace
allows for higher-order approximations, but it starts with a symbolic expression and substitutes numerical values. I’m starting with numerical values. Is my only option to generate TimeSeries
objects with symbolic values, do all my computations, and then AroundReplace
the symbols with the original numerical data?
Is there a better way?
————————————UPDATE————————————
@JulienKluge, @SjoerdSmit, and @JimB have suggested and demonstrated that this is a bug in Accumulate
. I’ve done my own investigation, and I agree. Accumulate
produces incorrectly propagated uncertainty whenever the same Around
object appears multiple times in whatever is being accumulated. This error seems to increase with the number of duplicates.
Accumulate
is not the only function with an issue. When the same Around
object appears consecutively in a list, Differences
produces a zero with no uncertainty!
(If you’d like a copy of the notebook in which I performed this investigation, click here.)
I’ve encountered the problem with Differences
before, but I’m only now recognizing it as a serious issue. I suppose bugs like this are why Around
is still an experimental function…
In light of everything, I think I will try doing all my calculations on symbolic TimeSeries
objects and finishing them off with AroundReplace
. Hopefully, that will circumvent the Accumulate
and Differences
issues as well as some other minor problems I’ve encountered. If that doesn’t work, I’ll compute the uncertainty bands myself…
Wish me luck!
————————————UPDATE————————————
AroundReplace
took over ten minutes to replace the values of a symbolic TimeSeries
with Around
objects. No other functions such as Accumulate
had yet been used. So, I won’t be using AroundReplace
. ReplaceAll
was much faster, but the documentation suggests it might introduce its own problems when working with Around
.
Following the advice of @SjoerdSmit, I am going to try using FoldList[Plus]@list
and MovingMap[Subtract[#[[2]], #[[1]]] &, #, 1] &@list
as alternatives to Accumulate
and Differences
, respectively.
I’ll report back if there are any major changes.
Yes, this seems to be a bug. Compare, for example:
list1 = {Around[1., 2.], Around[1., 2.0001]};
Plus @@ list1
Fold[Plus, 0, list1]
Last @ Accumulate[list1]
Around[2., 2.8284978363081703`]
with
list2 = {Around[1., 2.], Around[1., 2.]};
Plus @@ list2
Fold[Plus, 0, list2]
Last @ Accumulate[list2]
Around[2., 2.8284271247461903`]
Around[2., 4.] (* result from Accumulate*)
I reported the issue. In the mean while you can use:
FoldList[Plus, 0, list2]
to get a correctly accumulated list.
Correct answer by Sjoerd Smit on May 7, 2021
Okay, wow. This stumped me for a bit.
But I found the answer. It seems to be a bug. An innocent looking one.
The fact you tried Integers instead of Reals triggered it. (I tried to use Reals first and failed to reproduce)
What happens is the following. Whenever Accumulate sees two equal values, it multiplies them by two instead of adding them. Which makes sense for reals
Accumulate[{a, b}]
Accumulate[{a, a}]
{a, a + b}
{a, 2a}
But not for Around's. The multiplication by two yields a different error propagation in the standard linear Gaussian approach
Accumulate[{a,b}]/.{a->Around[v,d],b->Around[v,d]}
Accumulate[{a,a}]/.{a->Around[v,d]}
{v±d,2 v±Sqrt[2] Sqrt[d^2]}
{v±d,2 v±2 d}
Judging from the comments, seems to be a bug. Someone/You should file it with Wolfram.
12.1.0 for Microsoft Windows (64-bit) (March 14, 2020)
Answered by Julien Kluge on May 7, 2021
I don't have enough reputation yet to comment but I will note that Accumulate
can be particularly tricky with TimeSeries
objects. This is well documented elsewhere but, in short, Accumulate
applied to a TimeSeries
object will interpolate "missing" values if your TimeSeries
is not, by default, what Mathematica considers to be "Regularly Sampled". There are good reasons "under the hood" why this is the default behaviour but it's a trap for users who are inexperienced with the quirks of TimeSeries
To get around this, you can specify that your TimeSeries
object is regularly sampled when you create it.
For the purpose of this example, assume you have some data ready to be created as a timeseries assigned to xyz
:
newTimeseries = TimeSeries[xyz,TemporalRegularity->True]
When you now run Accumulate
on this newTimeSeries
object, you should get values as expected...but you may break other functionality by forcing the TemporalRegularity to True if it actually isn't.
Answered by R110 on May 7, 2021
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