ARIMA Models in Visual Basic QuickStart Sample

Illustrates how to work with ARIMA time series models using classes in the Extreme.Statistics.TimeSeriesAnalysis namespace in Visual Basic.

View this sample in: C# F# IronPython

Option Infer On

Imports Extreme.DataAnalysis
Imports Extreme.Mathematics
Imports Extreme.Statistics.TimeSeriesAnalysis

' Illustrates the use of the ArimaModel class to perform
' estimation and forecasting of ARIMA time series models.
Module ArimaModels

    Sub Main()
        ' The license is verified at runtime. We're using
        ' a demo license here. For more information, see
        ' https://numerics.net/trial-key
        Extreme.License.Verify("Demo license")

        ' This QuickStart Sample fits an ARMA(2,1) model and
        ' an ARIMA(0,1,1) model to sunspot data.

        ' The time series data is stored in a numerical variable:
        Dim sunspots = Vector.Create(
                100.8, 81.6, 66.5, 34.8, 30.6, 7, 19.8, 92.5,
                154.4, 125.9, 84.8, 68.1, 38.5, 22.8, 10.2, 24.1, 82.9,
                132, 130.9, 118.1, 89.9, 66.6, 60, 46.9, 41, 21.3, 16,
                6.4, 4.1, 6.8, 14.5, 34, 45, 43.1, 47.5, 42.2, 28.1, 10.1,
                8.1, 2.5, 0, 1.4, 5, 12.2, 13.9, 35.4, 45.8, 41.1, 30.4,
                23.9, 15.7, 6.6, 4, 1.8, 8.5, 16.6, 36.3, 49.7, 62.5, 67,
                71, 47.8, 27.5, 8.5, 13.2, 56.9, 121.5, 138.3, 103.2,
                85.8, 63.2, 36.8, 24.2, 10.7, 15, 40.1, 61.5, 98.5, 124.3,
                95.9, 66.5, 64.5, 54.2, 39, 20.6, 6.7, 4.3, 22.8, 54.8,
                93.8, 95.7, 77.2, 59.1, 44, 47, 30.5, 16.3, 7.3, 37.3,
                73.9)

        ' ARMA models (no differencing) are constructed from
        ' the variable containing the time series data, and the
        ' AR and MA orders. The following constructs an ARMA(2,1)
        ' model:
        Dim model As New ArimaModel(sunspots, 2, 1)

        ' The Fit methods fit the model.
        model.Fit()

        ' The model's Parameters collection contains the fitted values.
        ' For an ARIMA(p,d,q) model, the first p parameters are the 
        ' auto-regressive parameters. The last q parametere are the
        ' moving average parameters.
        Console.WriteLine("Variable              Value    Std.Error  t-stat  p-Value")
        For Each param As Parameter(Of Double) In model.Parameters
            ' Parameter objects have the following properties:
            ' - Name, usually the name of the variable:
            ' - Estimated value of the param:
            ' - Standard error:
            ' - The value of the t statistic for the hypothesis that the param
            '   is zero.
            ' - Probability corresponding to the t statistic.
            Console.WriteLine("{0,-20}{1,10:F5}{2,10:F5}{3,8:F2} {4,7:F4}",
                    param.Name,
                    param.Value,
                    param.StandardError,
                    param.Statistic,
                    param.PValue)
        Next

        ' The log-likelihood of the computed solution is also available:
        Console.WriteLine("Log-likelihood: {0:F4}", model.LogLikelihood)
        ' as is the Akaike Information Criterion (AIC):
        Console.WriteLine("AIC: {0:F4}", model.GetAkaikeInformationCriterion())
        ' and the Baysian Information Criterion (BIC):
        Console.WriteLine("AIC: {0:F4}", model.GetBayesianInformationCriterion())

        ' The Forecast method can be used to predict the next value in the series...
        Dim nextValue As Double = model.Forecast()
        Console.WriteLine("One step ahead forecast: {0:F3}", nextValue)

        ' or to predict a specified number of values:
        Dim nextValues = model.Forecast(5)
        Console.WriteLine("First five forecasts: {0:F3}", nextValues)


        ' An integrated model (with differencing) is constructed
        ' by supplying the degree of differencing. Note the order
        ' of the orders is the traditional one for an ARIMA(p,d,q)
        ' model (p, d, q).
        ' The following constructs an ARIMA(0,1,1) model:
        Dim model2 As New ArimaModel(sunspots, 0, 1, 1)

        ' By default, the mean is assumed to be zero for an integrated model.
        ' We can override this by setting the EstimateMean property to true:
        model2.EstimateMean = True

        ' The Fit methods fit the model.
        model2.Fit()

        ' The mean shows up as one of the parameters.
        Console.WriteLine("Variable              Value    Std.Error  t-stat  p-Value")
        For Each param As Parameter(Of Double) In model2.Parameters
            Console.WriteLine("{0,-20}{1,10:F5}{2,10:F5}{3,8:F2} {4,7:F4}",
                    param.Name,
                    param.Value,
                    param.StandardError,
                    param.Statistic,
                    param.PValue)
        Next

        ' We can also get the error variance:
        Console.WriteLine("Error variance: {0:F4}", model2.ErrorVariance)

        Console.WriteLine("Log-likelihood: {0:F4}", model2.LogLikelihood)
        Console.WriteLine("AIC: {0:F4}", model2.GetAkaikeInformationCriterion())
        Console.WriteLine("BIC: {0:F4}", model2.GetBayesianInformationCriterion())

        Console.WriteLine("Press Enter key to continue.")
        Console.ReadLine()
    End Sub

End Module