Class FittingInfo

FittingInfo

Be careful as the objective function is different for the static estimation that considers the absolute values, while dynamic estimation considers "diffs"- for this reason it is best to use RsqDiff and RsqAbs when comparing different model runs which can be a combination fo static and dynamic

Inheritance

object

FittingInfo

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Namespace: TimeSeriesAnalysis.Dynamic

Assembly: TimeSeriesAnalysis.dll

Syntax

public class FittingInfo

Fields

TimeBase_s

The time base of the fitting dataset (model can still be run on other timebases)

Declaration

public double TimeBase_s

Field Value

Type	Description
double

Umax

The maximum value of u seen in the data set

Declaration

public double[] Umax

Field Value

Type	Description
double[]

Umin

The minimum value of u seen in the data set

Declaration

public double[] Umin

Field Value

Type	Description
double[]

Properties

EndTime

End time of fitting data set

Declaration

public DateTime EndTime { get; set; }

Property Value

Type	Description
DateTime

FitScorePrc

A score that is 100% if model describes all variations and 0% if model is no better at describing variation than the flat average line. Negative if the model is worse than a flat average line.

Declaration

public double FitScorePrc { get; set; }

Property Value

Type	Description
double

NFittingBadDataPoints

Number of bad data points ignored during fitting

Declaration

public double NFittingBadDataPoints { get; set; }

Property Value

Type	Description
double

NFittingTotalDataPoints

Number of total data points (good and bad) available for fitting

Declaration

public double NFittingTotalDataPoints { get; set; }

Property Value

Type	Description
double

ObjFunValAbs

The value of the objective function during fitting, lower is better(used to choose among models)

This is the R-squared of the "absolute" sum(ymeas[k]-ymod[k-1])

>

Declaration

public double ObjFunValAbs { get; set; }

Property Value

Type	Description
double

ObjFunValDiff

The value of the objective function during fitting, lower is better(used to choose among models)

This is the R-squared of the "differences" sum(ymeas[k]-ymeas[k-1] -(ymod[k]-ymod[k-1]) )

>

Declaration

public double ObjFunValDiff { get; set; }

Property Value

Type	Description
double

RsqAbs

The value of the R2 or root mean square

This is the R-squared of the "absolute" sum(ymeas[k] - ymod[k] )

>

Declaration

public double RsqAbs { get; set; }

Property Value

Type	Description
double

RsqDiff

The value of the R2 or root mean square of fitting,higher is better (used to choose among models)

This is the R-squared of the "differences" sum(ymeas[k]-ymeas[k-1] -(ymod[k]-ymod[k-1]) )

>

Declaration

public double RsqDiff { get; set; }

Property Value

Type	Description
double

SolverID

A string that identifies the solver that was used to find the model

Declaration

public string SolverID { get; set; }

Property Value

Type	Description
string

StartTime

Start time of fitting data set

Declaration

public DateTime StartTime { get; set; }

Property Value

Type	Description
DateTime

WasAbleToIdentify

True if identification was able to identify, otherwise false. Note that this flag is not an indication that the model is good, i.e. that the data had sufficient information to determine unique paramters that describe the dataset well. This flag only indicates that regression did not crash during identification.

Declaration

public bool WasAbleToIdentify { get; set; }

Property Value

Type	Description
bool

Methods

CalcCommonFitMetricsFromYmeasDataset(UnitDataSet, List<int>)

NB! this code seems to have an error with negative rsqdiff for cases when there yIndicesToIgnore is not empty. It may be preferable to use the output of the regression, as this avoids duplicating logic.

Declaration

public void CalcCommonFitMetricsFromYmeasDataset(UnitDataSet dataSet, List<int> yIndicesToIgnore = null)

Parameters

Type	Name	Description
UnitDataSet	dataSet
List<int>	yIndicesToIgnore