FillOutliers

This package is a simpler Julia implementation of the Matlab function filloutliers.

This packages finds the outliers in a vector and fills them with a linear interpolation between closest non outlier/NaN/missing neighbours.

Three different methods are implemented to find the outliers:

• median: Outliers are those values that are 3 times the standard deviation calculated based on mad, std, away from the median.
• mean: Outliers are those values that are 3 times the standard deviation, std, away from the mean.
• quartiles: Outliers are those values that are outside the 25 and 75 quartile.
• moving mean: Outliers are those values that are 3 times the standard deviation, std, away from the moving mean. In that case the window size is window and needs to be provided as input.
• moving median: Outliers are those values that are 3 times the standard deviation calculated based on mad, std, away from the moving median. In that case the window size is window and needs to be provided as input.

The input data must be a a vector of numbers (1xn or nx1). The output is a vector of the same size as the input, converted to Float64. The input of matrices is not supported, but it can be done by applying the function to each column/row.

In this version the if teh input data starts or finishes with a series of outliers, the first and last values will be filled with the first and last non outlier value of the data vector.

Example: filloutliers(data, method, window) data = [1, 2, 100000, 4, 5, 7, 9, 15, NaN, 8, 9]

method = "mean"
window = nothing
# mean method
expected_mean = [1.0, 2.0, 100000.0, 4.0, 5.0, 7.0, 9.0, 15.0, 11.5, 8.0, 9.0]

method = "quartiles"
window = nothing
# quartiles method
expected_quartiles = [1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 9.0, 15.0, 11.5, 8.0, 9.0]

method = "moving mean"
window = 3
# moving mean method
expected_moving_mean = [1.0, 2.0, 100000.0, 4.0, 5.0, 7.0, 9.0, 15.0, 11.5, 8.0, 9.0]