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Detect and replace outliers in data

`B = filloutliers(A,fillmethod)`

`B = filloutliers(A,fillmethod,findmethod)`

`B = filloutliers(A,fillmethod,movmethod,window)`

`B = filloutliers(___,dim)`

`B = filloutliers(___,Name,Value)`

```
[B,TF,lower,upper,center]
= filloutliers(___)
```

finds
outliers in `B`

= filloutliers(`A`

,`fillmethod`

)`A`

and replaces them according to `fillmethod`

.
For example, `filloutliers(A,'previous')`

replaces
outliers with the previous non-outlier element. By default, an outlier
is a value that is more than three scaled median absolute deviations (MAD) away
from the median. If `A`

is a matrix or table, then `filloutliers`

operates
on each column separately. If `A`

is a multidimensional
array, then `filloutliers`

operates along the first
dimension whose size does not equal 1.

specifies
a method for determining outliers. For example, `B`

= filloutliers(`A`

,`fillmethod`

,`findmethod`

)`filloutliers(A,'previous','mean')`

defines
an outlier as an element of `A`

more than three standard
deviations from the mean.

specifies
a moving method for determining local outliers according to a window
length defined by `B`

= filloutliers(`A`

,`fillmethod`

,`movmethod`

,`window`

)`window`

. For example, `filloutliers(A,'previous','movmean',5)`

identifies
outliers as elements more than three local standard deviations away
from the local mean within a five-element window.

specifies
additional parameters for detecting and replacing outliers using one
or more name-value pair arguments. For example, `B`

= filloutliers(___,`Name,Value`

)`filloutliers(A,'previous','SamplePoints',t)`

detects
outliers in `A`

relative to the corresponding elements
of a time vector `t`

.

`[`

also returns information
about the position of the outliers and thresholds computed by the
detection method. `B`

,`TF`

,`lower`

,`upper`

,`center`

]
= filloutliers(___)`TF`

is a logical array indicating
the location of the outliers in `A`

. The `lower`

, `upper`

,
and `center`

arguments represent the lower and upper
thresholds and the center value used by the outlier detection method.

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