TimeSeries Class

Represents an ordered series of data points.

Definition

Namespace: Meta.Numerics.Statistics
Assembly: Meta.Numerics (in Meta.Numerics.dll) Version: 4.2.0+6d77d64445f7d5d91b12e331399c4362ecb25333

C#

public sealed class TimeSeries : IEnumerable, 
	IEnumerable<double>, IReadOnlyCollection<double>, IList<double>, 
	ICollection<double>

VB

Public NotInheritable Class TimeSeries
	Implements IEnumerable, IEnumerable(Of Double), 
	IReadOnlyCollection(Of Double), IList(Of Double), ICollection(Of Double)

C++

public ref class TimeSeries sealed : IEnumerable, 
	IEnumerable<double>, IReadOnlyCollection<double>, IList<double>, 
	ICollection<double>

F#

[<SealedAttribute>]
type TimeSeries = 
    class
        interface IEnumerable
        interface IEnumerable<float>
        interface IReadOnlyCollection<float>
        interface IList<float>
        interface ICollection<float>
    end

Inheritance: Object TimeSeries

Implements: ICollectionDouble, IEnumerableDouble, IListDouble, IReadOnlyCollectionDouble, IEnumerable

Remarks

NOTE: This class will be retired in a future release. Its functionality is replaced by the Series class, which can operatate on arbitrary data lists.

Constructors

TimeSeries	Initializes a new, empty time series.
TimeSeries(Double)	Initializes a new time series with the given values.

Properties

Count	Gets the number of points in the time series.
Item	Gets or sets the value of the time series at a given index.
Mean	Gets the mean of the time series.

Methods

Add(Double)	Adds a point to the time series.
Add(Double)	Adds multiple points to the time series.
AsSample	Gets a sample containing the time-series values.
Autocovariance	Computes the autocovariance for all lags.
Autocovariance(Int32)	Computes the autocovariance of the series at the given lag.
Clear	Removes all points from the time series.
Contains	Determines whether the time series contains a value.
Difference	Re-computes the time series as the differences between sequential values of the original series.
Equals	Determines whether the specified object is equal to the current object. (Inherited from Object)
FitToAR1	Fits an AR(1) model to the time series.
FitToMA1	Fits an MA(1) model to the time series.
GetHashCode	Serves as the default hash function. (Inherited from Object)
GetType	Gets the Type of the current instance. (Inherited from Object)
IndexOf	Finds the index at which a value occurs.
Integrate	Re-computes the time series as the sums of sequential values of the original series.
LjungBoxTest	Performs a Ljung-Box test for non-correlation.
LjungBoxTest(Int32)	Performs a Ljung-Box test for non-correlation with the given number of lags.
PopulationStatistics	Computes estimates for the moments of the population from which the time series is drawn.
PowerSpectrum	Computes the power spectrum of the time series.
ToString	Returns a string that represents the current object. (Inherited from Object)

Extension Methods

CentralMoment	Computes the given sample central moment. (Defined by Univariate)
CorrectedStandardDeviation	Computes the Bessel-corrected standard deviation. (Defined by Univariate)
Maximum	Finds the maximum value. (Defined by Univariate)
Mean	Computes the sample mean. (Defined by Univariate)
Minimum	Finds the minimum value. (Defined by Univariate)
PopulationCentralMoment	Estimates the given central moment of the underlying population. (Defined by Univariate)
PopulationMean	Estimates the mean of the underlying population. (Defined by Univariate)
PopulationRawMoment	Estimates the given raw moment of the underlying population. (Defined by Univariate)
PopulationStandardDeviation	Estimates of the standard deviation of the underlying population. (Defined by Univariate)
PopulationVariance	Estimates of the variance of the underlying population. (Defined by Univariate)
RawMoment	Computes the given sample raw moment. (Defined by Univariate)
SignTest	Tests whether the sample median is compatible with the given reference value. (Defined by Univariate)
Skewness	Computes the sample skewness. (Defined by Univariate)
StandardDeviation	Computes the sample standard deviation. (Defined by Univariate)
StudentTTest	Tests whether the sample mean is compatible with the reference mean. (Defined by Univariate)
Variance	Computes the sample variance. (Defined by Univariate)
ZTest	Performs a z-test to test whether the given sample is compatible with the given normal reference population. (Defined by Univariate)