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RegularlySampledQ[tes]

gives True if tes is a regular time or event series data, and False otherwise.

Details
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Examples  
Basic Examples  
Scope  
Properties & Relations  
Possible Issues  
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RegularlySampledQ

RegularlySampledQ[tes]

gives True if tes is a regular time or event series data, and False otherwise.

Details

  • RegularlySampledQ is typically used to check whether a time or event series has uniformly spaced timestamps.
  • A time series is regular if the sampling increment is some constant .
  • Possible forms of time or event series data tes include:
  • TimeSeries[…]continuous time-ordered sampled data
    EventSeries[…]collection of temporal events with values
    TemporalData[…]one or more paths composed of time-value pairs
    {{t1,x1},{t2,x2},…}list of time-value pairs
    {x1,x2,…}list of values with implied integer times starting at 0
  • For a TemporalData object with multiple paths, RegularlySampledQ returns True if all paths in the ensemble are regular.

Examples

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Basic Examples  (3)

Check whether a time series is uniformly spaced:

Wolfram Language code: TimeSeries[Range[4], {Today + Quantity[{1, 3, 7, 8}, "Days"]}]
Wolfram Language code: RegularlySampledQ[%]

A time series with timestamps given in the iterator form is regular:

Wolfram Language code: TimeSeries[Range[10], {Automatic, Today, "BusinessDay"}]
Wolfram Language code: RegularlySampledQ[%]

Check if a multipath TemporalData object is regularly sampled:

Wolfram Language code: RandomFunction[PoissonProcess[3], {1, 100}, 3]
Wolfram Language code: RegularlySampledQ[%]
Wolfram Language code: RandomFunction[WienerProcess[], {0, 1, .1}, 3]
Wolfram Language code: RegularlySampledQ[%]

Scope  (5)

Vector data is always regular:

Wolfram Language code: RegularlySampledQ[{Subscript[x, 1], Subscript[x, 2], Subscript[x, 3], Subscript[x, 4], Subscript[x, 5]}]

Test the regularity of time-value pairs:

Wolfram Language code: RegularlySampledQ[{{1, Subscript[``v``, 1]}, {2, Subscript[``v``, 2]}, {3, Subscript[``v``, 3]}}]
Wolfram Language code: RegularlySampledQ[{{1, Subscript[``v``, 1]}, {2.5, Subscript[``v``, 2]}, {3, Subscript[``v``, 3]}}]

Determine whether a TimeSeries is regularly spaced:

Wolfram Language code: TimeSeries[Range[5], {Array[Prime, 5]}]
Wolfram Language code: RegularlySampledQ[%]

Determine whether an EventSeries is regularly spaced:

Wolfram Language code: EventSeries[1, {DayRange[DateObject[{2026}, "Year"], DateObject[{2027}, "Year"], "Holiday"]}]
Wolfram Language code: RegularlySampledQ[%]

Test an ensemble of paths for regularity:

Wolfram Language code: TemporalData[{{1, 2, 3}, {11, 22, 33, 44}}, {{{0.1, 0.2, 0.3}}, {Range[4]}}]
Wolfram Language code: RegularlySampledQ[%]
Wolfram Language code: TemporalData[{{1, 2, 3}, {11, 22, 33, 44}}, {{{0.1, 0.2, 0.5}}, {Range[4]}}]
Wolfram Language code: RegularlySampledQ[%]

Properties & Relations  (4)

A time series is regular if every time step is equal to the MinimumTimeIncrement:

Wolfram Language code: data = RandomFunction[ARMAProcess[{.1}, {.2}, 1], {1, 10}]
Wolfram Language code: Differences[data["Times"]]
Wolfram Language code: MinimumTimeIncrement[data]
Wolfram Language code: RegularlySampledQ[data]

In an irregular series, the differences of the timestamps will be different:

Wolfram Language code: RandomFunction[PoissonProcess[4], {1, 2}]
Wolfram Language code: Differences[%["Times"]]
Wolfram Language code: RegularlySampledQ[%%]

A time series can be made regular using TimeSeriesResample:

Wolfram Language code: data = TimeSeries[{6, 8, 10, 11, 12, 12}, {{1., 1.14, 1.22, 1.45, 1.75, 2.}}]
Wolfram Language code: RegularlySampledQ[data]
Wolfram Language code: TimeSeriesResample[data]
Wolfram Language code: RegularlySampledQ[%]

Coerce a time series to be treated as regular using the TemporalRegularity option:

Wolfram Language code: ts = TimeSeries[{1, 2, 3, 4}, {{1, 2, 2.5, 4}}]
Wolfram Language code: RegularlySampledQ[ts]
Wolfram Language code: TimeSeries[{1, 2, 3, 4}, {{1, 2, 2.5, 4}}, TemporalRegularity -> True]
Wolfram Language code: RegularlySampledQ[%]

Possible Issues  (2)

A time series may be regular in a given day type but irregular in another:

Wolfram Language code: data = TimeSeries[Range[10], {Automatic, Today, "BusinessDay"}]
Wolfram Language code: RegularlySampledQ[data]

The path does not follow business day steps:

Wolfram Language code: path = data["Path"]
Wolfram Language code: RegularlySampledQ[path]

The path follows standard days, while the original time series follows "BusinessDay":

Wolfram Language code: MinimumTimeIncrement /@ {path, data}

The TemporalRegularity option does not change the timestamps but overrides the regularity of the series:

Wolfram Language code: data = TimeSeries[{1, 2, 3, 4}, {{1, 2, 2.5, 4}}, TemporalRegularity -> True]
Wolfram Language code: data["Timestamps"]//Normal
Wolfram Language code: RegularlySampledQ[data]
Wolfram Research (2014), RegularlySampledQ, Wolfram Language function, https://reference.wolfram.com/language/ref/RegularlySampledQ.html.

Text

Wolfram Research (2014), RegularlySampledQ, Wolfram Language function, https://reference.wolfram.com/language/ref/RegularlySampledQ.html.

CMS

Wolfram Language. 2014. "RegularlySampledQ." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/RegularlySampledQ.html.

APA

Wolfram Language. (2014). RegularlySampledQ. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/RegularlySampledQ.html

BibTeX

@misc{reference.wolfram_2026_regularlysampledq, author="Wolfram Research", title="{RegularlySampledQ}", year="2014", howpublished="\url{https://reference.wolfram.com/language/ref/RegularlySampledQ.html}", note=[Accessed: 12-June-2026]}

BibLaTeX

@online{reference.wolfram_2026_regularlysampledq, organization={Wolfram Research}, title={RegularlySampledQ}, year={2014}, url={https://reference.wolfram.com/language/ref/RegularlySampledQ.html}, note=[Accessed: 12-June-2026]}