Class BadDataFinder

Namespace

TimeSeriesAnalysis.Dynamic

Assembly

TimeSeriesAnalysis.dll

For dynamic model identification in system identification, bad data points will create "spurious dynamics" that it is especially important to filter out, otherwise it may destroy identification of dynamic terms.

public static class BadDataFinder

Inheritance

object

BadDataFinder

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Methods

GetAllBadIndices(double[], double)

Get all the values which are NaN or equal to the badValueIndicatingValue for a single vector/array

public static List<int> GetAllBadIndices(double[] inData, double badValueIndicatingValue)

Parameters

inData double[]

badValueIndicatingValue double

Returns

List<int>

GetAllBadIndices(TimeSeriesDataSet, double)

Get all the values which are NaN or equal to the badValueIndicatingValue for any and all datapoints in an entire TimeSeriesDataSet

public static List<int> GetAllBadIndices(TimeSeriesDataSet inputData, double badValueIndicatingValue)

Parameters

inputData TimeSeriesDataSet

badValueIndicatingValue double

Returns

List<int>

GetAllBadIndicesPlusNext(double[], double)

Get the bad value indices AND the indices trailing them. This is useful when considering difference equations that require the values both at index k and k-1 to perform identification.

public static List<int> GetAllBadIndicesPlusNext(double[] inData, double badValueIndicatingValue)

Parameters

inData double[]

badValueIndicatingValue double

Returns

List<int>