SeriesT Class

Represents a named series.


Namespace: Austra.Library
Assembly: Austra.Library (in Austra.Library.dll) Version: 2.5.0+d1268756e036101f601080f153b3997e72fc912d
public class Series<T> : ISafeIndexed
where T : struct, new(), IComparable<T>
Object    SeriesT

Type Parameters

Type of the abscissa.


SeriesTCreates a named series.


ArgsGets the list of arguments from the series.
CountGets the number of points in the series.
EnumValuesGets the list of values from the series.
FirstGets the first point in the series.
ItemIndexGets a point given its index.
ItemInt32Gets a point given its index.
ItemRangeExtracts a slice from the series.
KurtosisGets the unbiased population kurtosis.
LastGets the last point in the series.
MaximumReturns the maximum value from the series.
MeanGets the mean value from the series.
MinimumReturns the minimum value from the series.
NameGets the name of the series.
PointsGets the sorted list of points.
PopulationKurtosisGets the kurtosis from the full population.
PopulationSkewnessGet the skewness from the full population.
PopulationStandardDeviationGets the standard deviation from the full population.
PopulationVarianceGets the variance from the full population.
SkewnessGets the unbiased population skewness.
StandardDeviationGets the unbiased standard deviation.
StatsGets statistics on the series.
TickerGets the ticker of the series.
TypeIs this a raw (Prices) series or a derived one?
ValuesGets the values array as a vector.
VarianceGets the unbiased variance.


AbsMaxGets the maximum absolute value.
AbsMinGets the minimum absolute value.
ACFComputes autocorrelation for all lags.
AsLogReturnsCreates a new series based in the logarithmic returns.
AsReturnsCreates a new series based in the returns.
AutoCorrelationComputes the autocorrelation for a fixed lag.
AutoRegressionFinds the coefficients for an autoregressive model.
CombineCalculates the weighted sum of an array of series.
ContainsChecks if the series contains the given value.
CorrelationComputes the Pearson correlation between two series.
CorrelationMatrixComputes the correlation matrix for a group of series.
CorrelogramComputes autocorrelation for a range of lags.
CovarianceComputes the covariance between two series.
CovarianceMatrixComputes the covariance matrix for a group of series.
CreateCreates a series with integer arguments given its values.
EqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object)
FftComputes the real discrete Fourier transform.
GetHashCodeServes as the default hash function.
(Inherited from Object)
GetSliceStatsGets statistics on a slice of the series.
GetTypeGets the Type of the current instance.
(Inherited from Object)
IndexOfReturns the zero-based index of the first occurrence of a value.
LinearModelMultilinear regression based in Ordinary Least Squares.
MovingAverageFinds the coefficients for a moving average model.
NCdfThe normal cumulative distribution function of the most recent value.
NCdf(Double)The normal cumulative distribution function.
PACFComputes the partial autocorrelation for all lags.
PercentilesReturns ascendenly sorted values.
SafeThis Safe access to the series' points. If the index is out of range, a zero is returned.
SliceTakes a slice from a series.
SumCalculates the sum of the series values.
ToStringGets a textual representation of the series.
(Overrides ObjectToString)


Addition(SeriesT, SeriesT)Creates a new series by adding values from the operands.
Multiply(Double, SeriesT)Scales values from a series.
Multiply(SeriesT, Double)Scales values from a series.
Subtraction(SeriesT, SeriesT)Creates a new series by subtracting values from the operands.

See Also