/// <summary>
/// Differentiate procedures for forecast and estimate ARIMA.
/// </summary>
/// <param name="params"> ARIMA parameters </param>
/// <param name="dataForecastStationary"> stationary forecast data </param>
/// <param name="hasSeasonalI"> has seasonal I or not based on the parameter </param>
/// <param name="hasNonSeasonalI"> has NonseasonalI or not based on the parameter </param>
/// <returns> merged forecast data </returns>
private static double[] integrate(ArimaParams @params, double[] dataForecastStationary, bool hasSeasonalI, bool hasNonSeasonalI)
{
double[] forecast_merged;
if (hasSeasonalI && hasNonSeasonalI)
{
@params.IntegrateSeasonal(dataForecastStationary);
@params.IntegrateNonSeasonal(@params.LastIntegrateSeasonal);
forecast_merged = @params.LastIntegrateNonSeasonal;
}
else if (hasSeasonalI)
{
@params.IntegrateSeasonal(dataForecastStationary);
forecast_merged = @params.LastIntegrateSeasonal;
}
else if (hasNonSeasonalI)
{
@params.IntegrateNonSeasonal(dataForecastStationary);
forecast_merged = @params.LastIntegrateNonSeasonal;
}
else
{
forecast_merged = new double[dataForecastStationary.Length];
Array.Copy(dataForecastStationary, 0, forecast_merged, 0, dataForecastStationary.Length);
}
return(forecast_merged);
}
/// <summary>
/// Differentiate procedures for forecast and estimate ARIMA.
/// </summary>
/// <param name="params"> ARIMA parameters </param>
/// <param name="trainingData"> training data </param>
/// <param name="hasSeasonalI"> has seasonal I or not based on the parameter </param>
/// <param name="hasNonSeasonalI"> has NonseasonalI or not based on the parameter </param>
/// <returns> stationary data </returns>
private static double[] differentiate(ArimaParams @params, double[] trainingData, bool hasSeasonalI, bool hasNonSeasonalI)
{
double[] dataStationary; // currently un-centered
if (hasSeasonalI && hasNonSeasonalI)
{
@params.differentiateSeasonal(trainingData);
@params.DifferentiateNonSeasonal(@params.LastDifferenceSeasonal);
dataStationary = @params.LastDifferenceNonSeasonal;
}
else if (hasSeasonalI)
{
@params.differentiateSeasonal(trainingData);
dataStationary = @params.LastDifferenceSeasonal;
}
else if (hasNonSeasonalI)
{
@params.DifferentiateNonSeasonal(trainingData);
dataStationary = @params.LastDifferenceNonSeasonal;
}
else
{
dataStationary = new double[trainingData.Length];
Array.Copy(trainingData, 0, dataStationary, 0, trainingData.Length);
}
return(dataStationary);
}
/// <summary>
/// Set Sigma2(RMSE) and Predication Interval for forecast result.
/// </summary>
/// <param name="params"> ARIMA parameters </param>
/// <param name="forecastResult"> MODIFIED. forecast result </param>
/// <param name="forecastSize"> size of forecast </param>
/// <returns> max normalized variance </returns>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static double setSigma2AndPredicationInterval(final TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params, final TimeSeries.Forecast.TimeSeries.Arima.struct.ForecastResult forecastResult, final int forecastSize)
public static double setSigma2AndPredicationInterval(ArimaParams @params, ForecastResult forecastResult, int forecastSize)
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] coeffs_AR = params.getCurrentARCoefficients();
double[] coeffs_AR = @params.CurrentARCoefficients;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] coeffs_MA = params.getCurrentMACoefficients();
double[] coeffs_MA = @params.CurrentMACoefficients;
return(forecastResult.SetConfInterval(ForecastUtil.confidence_constant_95pct, ForecastUtil.getCumulativeSumOfCoeff(ForecastUtil.ARMAtoMA(coeffs_AR, coeffs_MA, forecastSize))));
}
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: private static TimeSeries.Forecast.matrix.InsightsVector iterationStep(final TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params, final double[] data, final double[] errors, final double[][] matrix, final int r, final int length, final int size)
private static InsightsVector iterationStep(ArimaParams @params, double[] data, double[] errors, double[][] matrix, int r, int length, int size)
{
int rowIdx = 0;
// copy over shifted timeseries data into matrix
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int[] offsetsAR = params.getOffsetsAR();
int[] offsetsAR = @params.OffsetsAR;
foreach (int pIdx in offsetsAR)
{
Array.Copy(data, r - pIdx, matrix[rowIdx], 0, size);
++rowIdx;
}
// copy over shifted errors into matrix
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int[] offsetsMA = params.getOffsetsMA();
int[] offsetsMA = @params.OffsetsMA;
foreach (int qIdx in offsetsMA)
{
Array.Copy(errors, r - qIdx, matrix[rowIdx], 0, size);
++rowIdx;
}
// instantiate matrix to perform least squares algorithm
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.matrix.InsightsMatrix zt = new TimeSeries.Forecast.matrix.InsightsMatrix(matrix, false);
InsightsMatrix zt = new InsightsMatrix(matrix, false);
// instantiate target vector
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] vector = new double[size];
double[] vector = new double[size];
Array.Copy(data, r, vector, 0, size);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.matrix.InsightsVector x = new TimeSeries.Forecast.matrix.InsightsVector(vector, false);
InsightsVector x = new InsightsVector(vector, false);
// obtain least squares solution
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.matrix.InsightsVector ztx = zt.timesVector(x);
InsightsVector ztx = zt.timesVector(x);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.matrix.InsightsMatrix ztz = zt.computeAAT();
InsightsMatrix ztz = zt.computeAAT();
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.matrix.InsightsVector estimatedVector = ztz.solveSPDIntoVector(ztx, TimeSeries.Forecast.timeseries.timeseriesutil.ForecastUtil.maxConditionNumber);
InsightsVector estimatedVector = ztz.solveSPDIntoVector(ztx, ForecastUtil.maxConditionNumber);
return(estimatedVector);
}
/// <summary>
/// Input checker
/// </summary>
/// <param name="params"> ARIMA parameter </param>
/// <param name="data"> original data </param>
/// <param name="startIndex"> start index of ARIMA operation </param>
/// <param name="endIndex"> end index of ARIMA operation </param>
/// <returns> whether the inputs are valid </returns>
private static bool checkARIMADataLength(ArimaParams @params, double[] data, int startIndex, int endIndex)
{
bool result = true;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int initialConditionSize = params.d + params.D * params.m;
int initialConditionSize = @params.d + @params.D * @params.m;
if (data.Length < initialConditionSize || startIndex < initialConditionSize || endIndex <= startIndex)
{
result = false;
}
return(result);
}
/// <summary>
/// Creates the fitted ARIMA model based on the ARIMA parameters.
/// </summary>
/// <param name="params"> MODIFIED. ARIMA parameters </param>
/// <param name="data"> UNMODIFIED. the original time series before differencing / centering </param>
/// <param name="forecastStartIndex"> the index where the forecast starts. startIndex ≤ data.length </param>
/// <param name="forecastEndIndex"> the index where the forecast stops (exclusive). startIndex < endIndex </param>
/// <returns> fitted ARIMA model </returns>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaModel estimateARIMA(final TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params, final double[] data, final int forecastStartIndex, final int forecastEndIndex)
public static ArimaModel estimateARIMA(ArimaParams @params, double[] data, int forecastStartIndex, int forecastEndIndex)
{
if (!checkARIMADataLength(@params, data, forecastStartIndex, forecastEndIndex))
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int initialConditionSize = params.d + params.D * params.m;
int initialConditionSize = @params.d + @params.D * @params.m;
throw new Exception("not enough data for ARIMA. needed at least " + initialConditionSize + ", have " + data.Length + ", startIndex=" + forecastStartIndex + ", endIndex=" + forecastEndIndex);
}
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int forecast_length = forecastEndIndex - forecastStartIndex;
int forecast_length = forecastEndIndex - forecastStartIndex;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] data_train = new double[forecastStartIndex];
double[] data_train = new double[forecastStartIndex];
Array.Copy(data, 0, data_train, 0, forecastStartIndex);
//=======================================
// DIFFERENTIATE
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final boolean hasSeasonalI = params.D > 0 && params.m > 0;
bool hasSeasonalI = @params.D > 0 && @params.m > 0;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final boolean hasNonSeasonalI = params.d > 0;
bool hasNonSeasonalI = @params.d > 0;
double[] data_stationary = differentiate(@params, data_train, hasSeasonalI, hasNonSeasonalI); // currently un-centered
// END OF DIFFERENTIATE
//==========================================
//=========== CENTERING ====================
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double mean_stationary = TimeSeries.Forecast.timeseries.timeseriesutil.Integrator.computeMean(data_stationary);
double mean_stationary = Integrator.computeMean(data_stationary);
Integrator.shift(data_stationary, (-1) * mean_stationary);
//==========================================
//==========================================
// FORECAST
HannanRissanen.estimateARMA(data_stationary, @params, forecast_length, maxIterationForHannanRissanen);
return(new ArimaModel(@params, data, forecastStartIndex));
}
} // pure static class
/// <summary>
/// Raw-level ARIMA forecasting function.
/// </summary>
/// <param name="data"> UNMODIFIED, list of double numbers representing time-series with constant time-gap </param>
/// <param name="forecastSize"> integer representing how many data points AFTER the data series to be
/// forecasted </param>
/// <param name="params"> ARIMA parameters </param>
/// <returns> a ForecastResult object, which contains the forecasted values and/or error message(s) </returns>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static TimeSeries.Forecast.TimeSeries.Arima.struct.ForecastResult forecast_arima(final double[] data, final int forecastSize, TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params)
public static ForecastResult forecast_arima(double[] data, int forecastSize, ArimaParams @params)
{
try
{
int p = @params.p;
int d = @params.d;
int q = @params.q;
int P = @params.P;
int D = @params.D;
int Q = @params.Q;
int m = @params.m;
ArimaParams paramsForecast = new ArimaParams(p, d, q, P, D, Q, m);
ArimaParams paramsXValidation = new ArimaParams(p, d, q, P, D, Q, m);
// estimate ARIMA model parameters for forecasting
ArimaModel fittedModel = ArimaSolver.estimateARIMA(paramsForecast, data, data.Length, data.Length + 1);
// compute RMSE to be used in confidence interval computation
double rmseValidation = ArimaSolver.computeRMSEValidation(data, ForecastUtil.testSetPercentage, paramsXValidation);
fittedModel.RMSE = rmseValidation;
ForecastResult forecastResult = fittedModel.Forecast(forecastSize);
// populate confidence interval
forecastResult.Sigma2AndPredicationInterval = fittedModel.Params;
// add logging messages
forecastResult.Log("{" + "\"Best ModelInterface Param\" : \"" + fittedModel.Params.summary() + "\"," + "\"Forecast Size\" : \"" + forecastSize + "\"," + "\"Input Size\" : \"" + data.Length + "\"" + "}");
// successfully built ARIMA model and its forecast
return(forecastResult);
}
catch (Exception ex)
{
// failed to build ARIMA model
throw new Exception("Failed to build ARIMA forecast: " + ex.Message);
}
}
/// <summary>
/// Performs validation using Root Mean-Squared Error given a time series (with forecast) and
/// true values
/// </summary>
/// <param name="data"> UNMODIFIED. time series data being evaluated </param>
/// <param name="testDataPercentage"> percentage of data to be used to evaluate as test set </param>
/// <param name="params"> MODIFIED. parameter of the ARIMA model </param>
/// <returns> a Root Mean-Squared Error computed from the forecast and true data </returns>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static double computeRMSEValidation(final double[] data, final double testDataPercentage, TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params)
public static double computeRMSEValidation(double[] data, double testDataPercentage, ArimaParams @params)
{
int testDataLength = (int)(data.Length * testDataPercentage);
int trainingDataEndIndex = data.Length - testDataLength;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaModel result = estimateARIMA(params, data, trainingDataEndIndex, data.length);
ArimaModel result = estimateARIMA(@params, data, trainingDataEndIndex, data.Length);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] forecast = result.forecast(testDataLength).getForecast();
double[] forecast = result.Forecast(testDataLength).Forecast;
return(ComputeRMSE(data, forecast, trainingDataEndIndex, 0, forecast.Length));
}
} // pure static helper class
/// <summary>
/// Forecast ARMA
/// </summary>
/// <param name="params"> MODIFIED. ARIMA parameters </param>
/// <param name="dataStationary"> UNMODIFIED. the time series AFTER differencing / centering </param>
/// <param name="startIndex"> the index where the forecast starts. startIndex ≤ data.length </param>
/// <param name="endIndex"> the index where the forecast stops (exclusive). startIndex endIndex </param>
/// <returns> forecast ARMA data point </returns>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static double[] forecastARMA(final TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params, final double[] dataStationary, final int startIndex, final int endIndex)
public static double[] forecastARMA(ArimaParams @params, double[] dataStationary, int startIndex, int endIndex)
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int train_len = startIndex;
int train_len = startIndex;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int total_len = endIndex;
int total_len = endIndex;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] errors = new double[total_len];
double[] errors = new double[total_len];
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] data = new double[total_len];
double[] data = new double[total_len];
Array.Copy(dataStationary, 0, data, 0, train_len);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int forecast_len = endIndex - startIndex;
int forecast_len = endIndex - startIndex;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] forecasts = new double[forecast_len];
double[] forecasts = new double[forecast_len];
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int _dp = params.getDegreeP();
int _dp = @params.DegreeP;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int _dq = params.getDegreeQ();
int _dq = @params.DegreeQ;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int start_idx = (_dp > _dq) ? _dp : _dq;
int start_idx = (_dp > _dq) ? _dp : _dq;
for (int j = 0; j < start_idx; ++j)
{
errors[j] = 0;
}
// populate errors and forecasts
for (int j = start_idx; j < train_len; ++j)
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double forecast = params.forecastOnePointARMA(data, errors, j);
double forecast = @params.forecastOnePointARMA(data, errors, j);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double error = data[j] - forecast;
double error = data[j] - forecast;
errors[j] = error;
}
// now we can forecast
for (int j = train_len; j < total_len; ++j)
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double forecast = params.forecastOnePointARMA(data, errors, j);
double forecast = @params.forecastOnePointARMA(data, errors, j);
data[j] = forecast;
errors[j] = 0;
forecasts[j - train_len] = forecast;
}
// return forecasted values
return(forecasts);
}
/// <summary>
/// Produce forecast result based on input ARIMA parameters and forecast length.
/// </summary>
/// <param name="params"> UNMODIFIED. ARIMA parameters </param>
/// <param name="data"> UNMODIFIED. the original time series before differencing / centering </param>
/// <param name="forecastStartIndex"> the index where the forecast starts. startIndex ≤ data.length </param>
/// <param name="forecastEndIndex"> the index where the forecast stops (exclusive). startIndex < endIndex </param>
/// <returns> forecast result </returns>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static TimeSeries.Forecast.TimeSeries.Arima.struct.ForecastResult forecastARIMA(final TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params, final double[] data, final int forecastStartIndex, final int forecastEndIndex)
public static ForecastResult forecastARIMA(ArimaParams @params, double[] data, int forecastStartIndex, int forecastEndIndex)
{
if (!checkARIMADataLength(@params, data, forecastStartIndex, forecastEndIndex))
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int initialConditionSize = params.d + params.D * params.m;
int initialConditionSize = @params.d + @params.D * @params.m;
throw new Exception("not enough data for ARIMA. needed at least " + initialConditionSize + ", have " + data.Length + ", startIndex=" + forecastStartIndex + ", endIndex=" + forecastEndIndex);
}
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int forecast_length = forecastEndIndex - forecastStartIndex;
int forecast_length = forecastEndIndex - forecastStartIndex;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] forecast = new double[forecast_length];
double[] forecast = new double[forecast_length];
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] data_train = new double[forecastStartIndex];
double[] data_train = new double[forecastStartIndex];
Array.Copy(data, 0, data_train, 0, forecastStartIndex);
//=======================================
// DIFFERENTIATE
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final boolean hasSeasonalI = params.D > 0 && params.m > 0;
bool hasSeasonalI = @params.D > 0 && @params.m > 0;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final boolean hasNonSeasonalI = params.d > 0;
bool hasNonSeasonalI = @params.d > 0;
double[] data_stationary = differentiate(@params, data_train, hasSeasonalI, hasNonSeasonalI); // currently un-centered
// END OF DIFFERENTIATE
//==========================================
//=========== CENTERING ====================
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double mean_stationary = TimeSeries.Forecast.timeseries.timeseriesutil.Integrator.computeMean(data_stationary);
double mean_stationary = Integrator.computeMean(data_stationary);
Integrator.shift(data_stationary, (-1) * mean_stationary);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double dataVariance = TimeSeries.Forecast.timeseries.timeseriesutil.Integrator.computeVariance(data_stationary);
double dataVariance = Integrator.computeVariance(data_stationary);
//==========================================
//==========================================
// FORECAST
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] forecast_stationary = forecastARMA(params, data_stationary, data_stationary.length, data_stationary.length + forecast_length);
double[] forecast_stationary = forecastARMA(@params, data_stationary, data_stationary.Length, data_stationary.Length + forecast_length);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] data_forecast_stationary = new double[data_stationary.length + forecast_length];
double[] data_forecast_stationary = new double[data_stationary.Length + forecast_length];
Array.Copy(data_stationary, 0, data_forecast_stationary, 0, data_stationary.Length);
Array.Copy(forecast_stationary, 0, data_forecast_stationary, data_stationary.Length, forecast_stationary.Length);
// END OF FORECAST
//==========================================
//=========== UN-CENTERING =================
Integrator.shift(data_forecast_stationary, mean_stationary);
//==========================================
//===========================================
// INTEGRATE
double[] forecast_merged = integrate(@params, data_forecast_stationary, hasSeasonalI, hasNonSeasonalI);
// END OF INTEGRATE
//===========================================
Array.Copy(forecast_merged, forecastStartIndex, forecast, 0, forecast_length);
return(new ForecastResult(forecast, dataVariance));
}
/// <summary>
/// Estimate ARMA(p,q) parameters, i.e. AR-parameters: \phi_1, ... , \phi_p
/// MA-parameters: \theta_1, ... , \theta_q
/// Input data is assumed to be stationary, has zero-mean, aligned, and imputed
/// </summary>
/// <param name="data_orig"> original data </param>
/// <param name="params"> ARIMA parameters </param>
/// <param name="forecast_length"> forecast length </param>
/// <param name="maxIteration"> maximum number of iteration </param>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static void estimateARMA(final double[] data_orig, final TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params, final int forecast_length, final int maxIteration)
public static void estimateARMA(double[] data_orig, ArimaParams @params, int forecast_length, int maxIteration)
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] data = new double[data_orig.length];
double[] data = new double[data_orig.Length];
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int total_length = data.length;
int total_length = data.Length;
Array.Copy(data_orig, 0, data, 0, total_length);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int r = (params.getDegreeP() > params.getDegreeQ()) ? 1 + params.getDegreeP() : 1 + params.getDegreeQ();
int r = (@params.DegreeP > @params.DegreeQ) ? 1 + @params.DegreeP : 1 + @params.DegreeQ;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int length = total_length - forecast_length;
int length = total_length - forecast_length;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int size = length - r;
int size = length - r;
if (length < 2 * r)
{
throw new Exception("not enough data points: length=" + length + ", r=" + r);
}
// step 1: apply Yule-Walker method and estimate AR(r) model on input data
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] errors = new double[length];
double[] errors = new double[length];
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] yuleWalkerParams = applyYuleWalkerAndGetInitialErrors(data, r, length, errors);
double[] yuleWalkerParams = applyYuleWalkerAndGetInitialErrors(data, r, length, errors);
for (int j = 0; j < r; ++j)
{
errors[j] = 0;
}
// step 2: iterate Least-Square fitting until the parameters converge
// instantiate Z-matrix
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[][] matrix = new double[params.getNumParamsP() + params.getNumParamsQ()][size];
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] matrix = new double[params.NumParamsP + params.NumParamsQ][size];
double[][] matrix = RectangularArrays.ReturnRectangularDoubleArray(@params.NumParamsP + @params.NumParamsQ, size);
double bestRMSE = -1; // initial value
int remainIteration = maxIteration;
InsightsVector bestParams = null;
while (--remainIteration >= 0)
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.matrix.InsightsVector estimatedParams = iterationStep(params, data, errors, matrix, r, length, size);
InsightsVector estimatedParams = iterationStep(@params, data, errors, matrix, r, length, size);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final TimeSeries.Forecast.matrix.InsightsVector originalParams = params.getParamsIntoVector();
InsightsVector originalParams = @params.ParamsIntoVector;
@params.ParamsFromVector = estimatedParams;
// forecast for validation data and compute RMSE
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] forecasts = ArimaSolver.forecastARMA(params, data, length, data.length);
double[] forecasts = ArimaSolver.forecastARMA(@params, data, length, data.Length);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double anotherRMSE = ArimaSolver.computeRMSE(data, forecasts, length, 0, forecast_length);
double anotherRMSE = ArimaSolver.ComputeRMSE(data, forecasts, length, 0, forecast_length);
// update errors
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] train_forecasts = ArimaSolver.forecastARMA(params, data, r, data.length);
double[] train_forecasts = ArimaSolver.forecastARMA(@params, data, r, data.Length);
for (int j = 0; j < size; ++j)
{
errors[j + r] = data[j + r] - train_forecasts[j];
}
if (bestRMSE < 0 || anotherRMSE < bestRMSE)
{
bestParams = estimatedParams;
bestRMSE = anotherRMSE;
}
}
@params.ParamsFromVector = bestParams;
}
