This is the purpose of the TemporalRegularity option.

TemporalRegularity is an option for TemporalData, TimeSeries, and EventSeries that controls whether the paths are assumed to be uniformly spaced in time.

When setting this option, the dates themselves are ignored and a standard index {0,1,...,n} is used in its place, allowing for non-uniform dates/times to be used for labeling and plotting purposes while leveraging algorithms that require uniform spacing.

TimeSeries[
  Transpose@{DateRange[{2006, 12, 31}, {2007, 9, 30}, "EndOfMonth"], 
    Range[10]}, TemporalRegularity -> True] // RegularlySampledQ

(* True *)

This is the purpose of the TemporalRegularity option.

TemporalRegularity is an option for TemporalData, TimeSeries, and EventSeries that controls whether the paths are assumed to be uniformly spaced in time.

When setting this option, the dates themselves are ignored and a standard index {0,1,...,n} is used in its place, allowing for non-uniform dates/times to be used for labeling and plotting purposes while leveraging algorithms that require uniform spacing.

TimeSeries[
  Transpose@{DateRange[{2006, 12, 31}, {2007, 9, 30}, "EndOfMonth"], 
    Range[10]}, TemporalRegularity -> True] // RegularlySampledQ

(* True *)

Edit

It is interesting to look at the InputForm of the TimeSeries object that is produced by setting TemporalRegularity to True. Note the use of a uniform time specification.

TemporalData[TimeSeries, {{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}}, 
  {TemporalData`UniformTimeSpecification[{3376512000, 3379190400, 3381609600, 
    3384288000, 3386880000, 3389558400, 3392150400, 3394828800, 3397507200, 
    3400099200}]}, 1, {"Continuous", 1}, {"Discrete", 1}, 1, 
  {ValueDimensions -> 1, DateFunction -> Automatic, 
   ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}, 
   TemporalRegularity -> True}}, True, 10.1]

Obviously this is exposing internal functionality that is subject to change.