public void SetData(SeriesData data, SeriesVariable variable, double[] predicted)
{
this.variable = variable;
this.data = data;
this.predicted = predicted;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, double[] residual)
{
this.variable = variable;
this.data = data;
this.residual = residual;
this.update();
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="name">Stock Name</param>
/// <param name="description">Stock Description</param>
/// <param name="open">Stock's open variable</param>
/// <param name="close">Stock's close variable</param>
/// <param name="high">Stock's high variable</param>
/// <param name="low">Stock's low variable</param>
/// <param name="volume">Stock's volume variable</param>
private void initialize(string name, string description, SeriesVariable open, SeriesVariable close, SeriesVariable high, SeriesVariable low, SeriesVariable volume)
{
this.stockName = name;
this.stockDescription = description;
this.open = open;
this.close = close;
this.high = high;
this.low = low;
this.volume = volume;
if (this.open != null)
{
this.open.Changed += new ChangedEventHandler(changed);
}
if (this.close != null)
{
this.close.Changed += new ChangedEventHandler(changed);
}
if (this.high != null)
{
this.high.Changed += new ChangedEventHandler(changed);
}
if (this.low != null)
{
this.low.Changed += new ChangedEventHandler(changed);
}
if (this.volume != null)
{
this.volume.Changed += new ChangedEventHandler(changed);
}
}
public void SetData(SeriesData data, SeriesVariable variable, double[] deseasonal)
{
this.variable = variable;
this.data = data;
this.deseasonal = deseasonal;
this.update();
}
/// <summary>
/// Metode dekomposisi klasik dengan rasio pada rata-rata bergerak
/// </summary>
/// <param name="variable">SeriesVariable. variabel yang akan dianalisis</param>
/// <param name="seasonalLength">Integer. panjang satu siklus musiman</param>
/// <param name="isMultiplikatif">Bool. apakah menggunakan metode multiplikatif?</param>
/// <param name="initialTrend">Integer. Inisial model tren yang digunakan, 1-Linear, 2-Quadratic, 3-Cubic, 4-Exponential</param>
public DecompositionClassic(SeriesVariable variable, int seasonalLength, bool isMultiplicative, int initialTrend)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.y = new Vector(var.Tuples);
for (int i = 0; i < y.Tuples; i++)
this.y[i] = var[i];
this.n = y.Tuples;
this.seasonalLength = seasonalLength;
this.trend = new double[this.n];
this.detrend = new double[this.n];
this.seasonal = new double[this.n];
this.deseasonal = new double[this.n];
this.predicted = new double[this.n];
this.residual = new double[this.n];
this.isMultiplicative = isMultiplicative;
this.initialTrend = initialTrend;
if (this.isMultiplicative)
this.Multiplicative();
else
this.Additive();
this.ForecastingError();
}
public void SetData(SeriesData data, SeriesVariable variable, double[] detrend)
{
this.variable = variable;
this.data = data;
this.detrend = detrend;
this.update();
}
/// <summary>
/// Mencari index variabel pada list
/// </summary>
/// <param name="variableCollection">list variabel</param>
/// <param name="name">nama variabel</param>
/// <returns>index</returns>
public static int FindVariableIndex(SeriesVariables variableCollection, string name)
{
variableToFind = new SeriesVariable(name, "");
System.Predicate<SeriesVariable> search = findVariablePredicate;
return variableCollection.FindIndex(search);
}
public void SetData(SeriesData data, SeriesVariable variable, double[] forecasted)
{
this.variable = variable;
this.data = data;
this.forecasted = forecasted;
this.update();
}
/// <summary>
/// Mencari index variabel pada list
/// </summary>
/// <param name="variableCollection">list variabel</param>
/// <param name="name">nama variabel</param>
/// <returns>index</returns>
public static int FindVariableIndex(SeriesVariables variableCollection, string name)
{
variableToFind = new SeriesVariable(name, "");
System.Predicate <SeriesVariable> search = findVariablePredicate;
return(variableCollection.FindIndex(search));
}
public void SetData(SeriesVariable dependentVariable, SeriesVariables independentVariables, LinearRegressionAnalysisForm.LRSpecification lrProperties)
{
this.dependentVariable = dependentVariable;
this.independentVariables = independentVariables;
this.lrProperties = lrProperties;
this.update();
}
public void SetData(SeriesVariable variable, SeriesData data)
{
this.variable = variable;
this.data = data;
this.variable.SeriesValues.Changed += new ChangedEventHandler(SeriesValues_Changed);
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, MovingAverageForm.MASpecification maProperties)
{
this.data = data;
this.variable = variable;
this.maProperties = maProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesVariable variable, SeriesData data)
{
this.variableDataGrid.SetData(variable, data);
this.variableGraph.SetData(variable, data);
this.variableStatistics.SetData(variable, data);
this.Text = variable.VariableName;
this.Name = "SERIES " + variable.VariableName;
}
public void SetData(SeriesData data, SeriesVariable variable, TrendAnalysisForm.TrendSpecification trendProperties)
{
this.data = data;
this.variable = variable;
this.trendProperties = trendProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, NeuralNetworkAnalysisForm.NetworkSpecification networkProperties)
{
this.data = data;
this.variable = variable;
this.networkProperties = networkProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, ExponentialSmoothingForm.ESSpecification esProperties)
{
this.data = data;
this.variable = variable;
this.esProperties = esProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, DecompositionForm.DECSpecification decProperties)
{
this.data = data;
this.variable = variable;
this.decProperties = decProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
/// <summary>
/// Mengkloning variabel
/// </summary>
/// <returns>variabel baru dengan nilai sama dengan variabel asal</returns>
public object Clone()
{
SeriesVariable clone = new SeriesVariable(this.VariableName, this.VariableDescription, this.SeriesValues.Count);
for (int i = 0; i < this.SeriesValues.Count; i++)
{
clone[i] = this[i];
}
return(clone);
}
public void SetData(SeriesData data, SeriesVariable variable, bool isSmoothingVisible, double[] smoothed1, double[] predicted, double[] residual)
{
this.data = data;
this.variable = variable;
this.isSmoothingVisible = isSmoothingVisible;
this.smoothed1 = smoothed1;
this.predicted = predicted;
this.residual = residual;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, bool isSmoothingVisible, bool isTrendVisible, bool isSeasonalVisible, double[] smoothing, double[] trend, double[] seasonal, double[] predicted, double[] residual)
{
this.data = data;
this.variable = variable;
this.isSmoothingVisible = isSmoothingVisible;
this.isTrendVisible = isTrendVisible;
this.isSeasonalVisible = isSeasonalVisible;
this.smoothing = smoothing;
this.trend = trend;
this.seasonal = seasonal;
this.predicted = predicted;
this.residual = residual;
this.update();
}
/// <summary>
/// Constructor. Pemulusan dengan metode Rata-Rata Bergerak Tunggal
/// </summary>
/// <param name="variable">SeriesVariable. variable</param>
/// <param name="maLength">Integer. MA Length</param>
public SingleMovingAverage(SeriesVariable variable, int maLength)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.Y = new Vector(var.Tuples);
for (int i = 0; i < Y.Tuples; i++)
this.Y[i] = var[i];
this.n = Y.Tuples;
this.t = maLength;
this.smoothing1 = new double[this.n];
this.predicted = new double[this.n];
this.residual = new double[this.n];
this.Smoothing();
this.ForecastingError();
}
/// <summary>
/// Constructor. Menghitung Pemulusan Eksponensial Tripel
/// </summary>
/// <param name="variable">SeriesVariable. variabel</param>
/// <param name="alpha">Double. Smoothing constant for data</param>
/// <param name="gamma">Double. Smoothing constant for trend</param>
/// <param name="beta">Double. Smoothing constant for seasonal</param>
/// <param name="seasonalLength">Integer. Panjang seasonal</param>
public TripleExponentialSmoothingWinter(SeriesVariable variable, double alpha, double gamma, double beta, int seasonalLength, bool isMultiplicative)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.y = new Vector(var.Tuples);
for (int i = 0; i < y.Tuples; i++)
this.y[i] = var[i];
this.n = this.y.Tuples;
this.alpha = alpha;
this.gamma = gamma;
this.beta = beta;
this.l = seasonalLength;
this.isMultiplicative = isMultiplicative;
this.smoothing = new double[this.n];
this.trend = new double[this.n];
this.seasonal = new double[this.n];
this.predicted= new double[this.n];
this.residual = new double[this.n];
//term
term = new int[this.n];
for (int i = 0; i < this.n; )
{
for (int j = 0; j < this.l; ++j)
{
if (i + j != this.n)
term[i + j] = j;
else
break;
}
i += this.l;
}
if(this.isMultiplicative)
SmoothingMultiplicative();
else
SmoothingAdditive();
ForecastingError();
}
/// <summary>
/// Constructor. Pemulusan Eksponensial Ganda dengan metode Holt
/// </summary>
/// <param name="variable">SeriesVariable. variabel</param>
/// <param name="alpha">Double. Smoothing constanta for data</param>
/// <param name="gamma">Double. Smoothing constanta for trend</param>
public DoubleExponentialSmoothingHolt(SeriesVariable variable, double alpha, double gamma)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.y = new Vector(var.Tuples);
for (int i = 0; i < y.Tuples; i++)
this.y[i] = var[i];
this.n = this.y.Tuples;
this.a = alpha;
this.g = gamma;
this.smoothing = new double[this.n];
this.trend = new double[this.n];
this.predicted = new double[this.n];
this.residual = new double[this.n];
this.Smoothing();
this.ForecastingError();
}
/// <summary>
/// Pemulusan dengan metode Rata-Rata Bergerak ganda
/// </summary>
/// <param name="variable">SeriesVariable. variabel yang akan dianalisis</param>
/// <param name="maLength">Integer. MA Length</param>
public DoubleMovingAverage(SeriesVariable variable, int maLength)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.y = new Vector(var.Tuples);
for (int i = 0; i < y.Tuples; i++)
y[i] = var[i];
this.n = y.Tuples;
this.t = maLength;
this.smoothing1 = new double[n];
this.smoothing2 = new double[n];
this.at = new double[n];
this.bt = new double[n];
this.predicted = new double[n];
this.residual = new double[n];
Smoothing();
ForecastingError();
}
public void SetData(SeriesVariables independentVariables,
SeriesVariable dependentVariable,
double[,] testValues,
string[] forcastedTime,
double[] forcastedData)
{
foreach (SeriesVariable var in independentVariables)
{
DataGridViewTextBoxColumn clm = new DataGridViewTextBoxColumn();
clm.AutoSizeMode = System.Windows.Forms.DataGridViewAutoSizeColumnMode.AllCells;
clm.HeaderText = var.VariableName;
clm.Name = "clm" + var.VariableName;
clm.ReadOnly = true;
clm.SortMode = System.Windows.Forms.DataGridViewColumnSortMode.Programmatic;
this.grdForcasted.Columns.Add(clm);
}
DataGridViewTextBoxColumn c = new DataGridViewTextBoxColumn();
c.AutoSizeMode = System.Windows.Forms.DataGridViewAutoSizeColumnMode.AllCells;
c.HeaderText = dependentVariable.VariableName;
c.Name = "clm" + dependentVariable.VariableName;
c.ReadOnly = true;
c.SortMode = System.Windows.Forms.DataGridViewColumnSortMode.Programmatic;
this.grdForcasted.Columns.Add(c);
this.grdForcasted.RowCount = forcastedTime.Length;
for (int i = 0; i < this.grdForcasted.Rows.Count; i++)
{
this.grdForcasted.Rows[i].HeaderCell.Value = forcastedTime[i];
for (int j = 0; j < independentVariables.Count; j++)
{
this.grdForcasted.Rows[i].Cells[j].Value = testValues[j, i].ToString("F4");
}
this.grdForcasted.Rows[i].Cells[independentVariables.Count].Value = forcastedData[i].ToString("F4");
}
this.grdForcasted.RowHeadersWidthSizeMode = DataGridViewRowHeadersWidthSizeMode.AutoSizeToAllHeaders;
}
public void TransformVariable()
{
SeriesVariables vars = this.data.SeriesVariables;
zaitun.GUI.TransformVariable ser = new zaitun.GUI.TransformVariable(vars);
ser.ShowDialog();
if (ser.DialogResult == DialogResult.OK)
{
SeriesVariable tmpVar = new SeriesVariable(ser.NewVariableName,
ser.Transform.ToString() + " transformation" + " from \"" +
ser.SelectedVariable.VariableName + "\"");
if (ser.Transform == zaitun.GUI.TransformVariable.TransformationType.Differencing)
{
tmpVar.SeriesValues.Add(double.NaN);
for (int i = 1; i < ser.SelectedVariable.SeriesValues.Count; i++)
{
tmpVar.SeriesValues.Add(ser.SelectedVariable.SeriesValues[i] - ser.SelectedVariable.SeriesValues[i - 1]);
}
}
else if (ser.Transform == zaitun.GUI.TransformVariable.TransformationType.SeasonalDifferencing)
{
for (int i = 0; i < ser.SeasonalLag; i++)
tmpVar.SeriesValues.Add(double.NaN);
for (int i = ser.SeasonalLag; i < ser.SelectedVariable.SeriesValues.Count; i++)
{
tmpVar.SeriesValues.Add(ser.SelectedVariable.SeriesValues[i] - ser.SelectedVariable.SeriesValues[i - ser.SeasonalLag]);
}
}
else if (ser.Transform == zaitun.GUI.TransformVariable.TransformationType.NaturalLogarithm)
{
for (int i = 0; i < ser.SelectedVariable.SeriesValues.Count; i++)
{
tmpVar.SeriesValues.Add(Math.Log(ser.SelectedVariable.SeriesValues[i]));
}
}
else if (ser.Transform == zaitun.GUI.TransformVariable.TransformationType.Logarithm)
{
for (int i = 0; i < ser.SelectedVariable.SeriesValues.Count; i++)
{
tmpVar.SeriesValues.Add(Math.Log(ser.SelectedVariable.SeriesValues[i], ser.LogarithmBase));
}
}
else if (ser.Transform == zaitun.GUI.TransformVariable.TransformationType.SquareRoot)
{
for (int i = 0; i < ser.SelectedVariable.SeriesValues.Count; i++)
{
tmpVar.SeriesValues.Add(Math.Sqrt(ser.SelectedVariable.SeriesValues[i]));
}
}
try
{
vars.Add(tmpVar);
this.seriesDataList.Refresh();
}
catch (DuplicateVariableException)
{
MessageBox.Show("Variable '" + tmpVar.VariableName + "' already exist", "Duplicate Variable Name", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
}
public void MovingAverage()
{
SelectAnalyzedVariable dlg = new SelectAnalyzedVariable(this.data.SeriesVariables);
dlg.ShowDialog();
if (dlg.DialogResult == DialogResult.OK)
{
MovingAverageForm maForm = new MovingAverageForm();
maForm.SetVariable(dlg.SelectedVariable);
maForm.ShowDialog();
if (maForm.DialogResult == DialogResult.Yes)
{
if (maForm.MaProperties.isSingleMA)
{
if (maForm.IsStoreSmoothed)
{
SeriesVariable var = new SeriesVariable(maForm.SmoothedName, "Smoothed Value of MA(" + maForm.MaProperties.orde +
") analysis of variable '" + dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - maForm.MaProperties.includedObservations;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = lag; i < lag + maForm.MaProperties.orde; i++) var[i] = double.NaN;
for (int i = lag + maForm.MaProperties.orde; i < maForm.MaProperties.includedObservations + lag; i++) var[i] = maForm.MaTable.singleSmoothed[i - lag];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (maForm.IsStorePredicted)
{
SeriesVariable var = new SeriesVariable(maForm.PredictedName, "Predicted Value of MA(" + maForm.MaProperties.orde +
") analysis of variable '" + dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - maForm.MaProperties.includedObservations;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = lag; i < lag + maForm.MaProperties.orde; i++) var[i] = double.NaN;
for (int i = lag + maForm.MaProperties.orde; i < maForm.MaProperties.includedObservations + lag; i++) var[i] = maForm.MaTable.predicted[i - lag];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (maForm.IsStoreResidual)
{
SeriesVariable var = new SeriesVariable(maForm.ResidualName, "Residual Value of MA(" + maForm.MaProperties.orde +
") analysis of variable '" + dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - maForm.MaProperties.includedObservations;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = lag; i < lag + maForm.MaProperties.orde; i++) var[i] = double.NaN;
for (int i = lag + maForm.MaProperties.orde; i < maForm.MaProperties.includedObservations + lag; i++) var[i] = maForm.MaTable.residual[i - lag];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
}
else
{
if (maForm.IsStoreSmoothed)
{
SeriesVariable var = new SeriesVariable(maForm.SmoothedName, "Smoothed Value of MA(" + maForm.MaProperties.orde +
"X" + maForm.MaProperties.orde + ") analysis of variable '" + dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - maForm.MaProperties.includedObservations;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = lag; i < lag + (2 * (maForm.MaProperties.orde - 1)); i++) var[i] = double.NaN;
for (int i = lag + (2 * (maForm.MaProperties.orde - 1)); i < maForm.MaProperties.includedObservations + lag; i++) var[i] = maForm.MaTable.doubleSmoothed[i - lag];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (maForm.IsStorePredicted)
{
SeriesVariable var = new SeriesVariable(maForm.PredictedName, "Predicted Value of MA(" + maForm.MaProperties.orde +
"X" + maForm.MaProperties.orde + ") analysis of variable '" + dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - maForm.MaProperties.includedObservations;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = lag; i < lag + (2 * (maForm.MaProperties.orde - 1)); i++) var[i] = double.NaN;
for (int i = lag + (2 * (maForm.MaProperties.orde - 1)); i < maForm.MaProperties.includedObservations + lag; i++) var[i] = maForm.MaTable.predicted[i - lag];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (maForm.IsStoreResidual)
{
SeriesVariable var = new SeriesVariable(maForm.ResidualName, "Residual Value of MA(" + maForm.MaProperties.orde +
"," + maForm.MaProperties.orde + ") analysis of variable '" + dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - maForm.MaProperties.includedObservations;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = lag; i < lag + (2 * (maForm.MaProperties.orde - 1)); i++) var[i] = double.NaN;
for (int i = lag + (2 * (maForm.MaProperties.orde - 1)); i < maForm.MaProperties.includedObservations + lag; i++) var[i] = maForm.MaTable.residual[i - lag];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
}
MAResultTabPage maTabPage = new MAResultTabPage();
maTabPage.Title = "Moving Average : '" + dlg.SelectedVariable.VariableName + "'";
if (maForm.IsForecastedDataGridChecked)
{
if (maForm.MaProperties.isSingleMA)
{
maTabPage.SetData(this.data, dlg.SelectedVariable, maForm.MaProperties,
maForm.MaTable, maForm.SmaForecast(maForm.ForecastingStep));
}
else
{
maTabPage.SetData(this.data, dlg.SelectedVariable, maForm.MaProperties,
maForm.MaTable, maForm.DmaForecast(maForm.ForecastingStep));
}
}
else
{
maTabPage.SetData(this.data, dlg.SelectedVariable, maForm.MaProperties,
maForm.MaTable, null);
}
maTabPage.IsMaModelSummaryVisible = maForm.IsMaModelSummaryChecked;
maTabPage.IsSmoothingVisible = maForm.IsSmoothingChecked;
maTabPage.IsMovingAverageDataGridVisible = maForm.IsMovingAverageDataGridChecked;
maTabPage.IsForecastedDataGridVisible = maForm.IsForecastedDataGridChecked;
maTabPage.IsActualAndPredictedGraphVisible = maForm.IsActualAndPredictedGraphChecked;
maTabPage.IsActualAndPredictedGraphVisible = maForm.IsActualAndPredictedGraphChecked;
maTabPage.IsActualAndSmoothedGraphVisible = maForm.IsActualAndSmoothedGraphChecked;
maTabPage.IsActualAndForecastedGraphVisible = maForm.IsActualAndForecastedGraphChecked;
maTabPage.IsActualVsPredictedGraphVisible = maForm.IsActualVsPredictedGraphChecked;
maTabPage.IsResidualGraphVisible = maForm.IsResidualGraphChecked;
maTabPage.IsResidualVsActualGraphVisible = maForm.IsResidualVsActualGraphChecked;
maTabPage.IsResidualVsPredictedGraphVisible = maForm.IsResidualVsPredictedGraphChecked;
maTabPage.DrawControl();
maTabPage.IsDrawn = true;
this.tabControlResult.AddTab(maTabPage);
this.tabControlResult.SelectedItem = maTabPage;
this.tabControlData.SelectedTab = this.tabPageResult;
}
}
}
public void ExponentialSmoothing()
{
SelectAnalyzedVariable dlg = new SelectAnalyzedVariable(this.data.SeriesVariables);
dlg.ShowDialog();
if (dlg.DialogResult == DialogResult.OK)
{
ExponentialSmoothingForm esForm = new ExponentialSmoothingForm();
esForm.SetVariable(dlg.SelectedVariable);
esForm.ShowDialog();
if (esForm.DialogResult == DialogResult.Yes)
{
if (esForm.IsStoreSmoothed)
{
SeriesVariable var = new SeriesVariable(esForm.SmoothedName, "Smoothed Value of exponential smoothing of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - esForm.EsTable.predicted.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < esForm.EsTable.smoothed.Length; i++) var[i + lag] = esForm.EsTable.smoothed[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (esForm.IsStorePredicted)
{
SeriesVariable var = new SeriesVariable(esForm.PredictedName, "Predicted Value of exponential smoothing of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - esForm.EsTable.predicted.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < esForm.EsTable.predicted.Length; i++) var[i + lag] = esForm.EsTable.predicted[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (esForm.IsStoreResidual)
{
SeriesVariable var = new SeriesVariable(esForm.ResidualName, "Residual Value of exponential smoothing of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - esForm.EsTable.residual.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < esForm.EsTable.residual.Length; i++) var[i + lag] = esForm.EsTable.residual[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
ESResultTabPage esTabPage = new ESResultTabPage();
if (esForm.EsProperties.initialModel == 1)
esTabPage.Title = "Single ES : '" + dlg.SelectedVariable.VariableName + "'";
if (esForm.EsProperties.initialModel == 2)
esTabPage.Title = "Double ES (Brown) : '" + dlg.SelectedVariable.VariableName + "'";
if (esForm.EsProperties.initialModel == 3)
esTabPage.Title = "Double ES (Holt) : '" + dlg.SelectedVariable.VariableName + "'";
if (esForm.EsProperties.initialModel == 4)
esTabPage.Title = "Triple ES (Winter) : '" + dlg.SelectedVariable.VariableName + "'";
if (esForm.IsForecastedDataGridChecked)
{
if (esForm.EsProperties.initialModel == 1)
{
esTabPage.SetData(this.data, dlg.SelectedVariable, esForm.EsProperties,
esForm.EsTable, esForm.SesForecast(esForm.ForecastingStep));
}
if (esForm.EsProperties.initialModel == 2)
{
esTabPage.SetData(this.data, dlg.SelectedVariable, esForm.EsProperties,
esForm.EsTable, esForm.BrownForecast(esForm.ForecastingStep));
}
if (esForm.EsProperties.initialModel == 3)
{
esTabPage.SetData(this.data, dlg.SelectedVariable, esForm.EsProperties,
esForm.EsTable, esForm.HoltForecast(esForm.ForecastingStep));
}
if (esForm.EsProperties.initialModel == 4)
{
esTabPage.SetData(this.data, dlg.SelectedVariable, esForm.EsProperties,
esForm.EsTable, esForm.WinterForecast(esForm.ForecastingStep));
}
}
else
{
esTabPage.SetData(this.data, dlg.SelectedVariable, esForm.EsProperties,
esForm.EsTable, null);
}
esTabPage.IsEsModelSummaryVisible = esForm.IsEsModelSummaryChecked;
esTabPage.IsExponentialSmoothingDataGridVisible = esForm.IsExponentialSmoothingDataGridChecked;
esTabPage.IsSmoothingVisible = esForm.IsSmoothingChecked;
esTabPage.IsTrendVisible = esForm.IsTrendChecked;
esTabPage.IsSeasonalVisible = esForm.IsSeasonalChecked;
esTabPage.IsForecastedDataGridVisible = esForm.IsForecastedDataGridChecked;
esTabPage.IsActualAndPredictedGraphVisible = esForm.IsActualAndPredictedGraphChecked;
esTabPage.IsActualAndSmoothedGraphVisible = esForm.IsActualAndSmoothedGraphChecked;
esTabPage.IsActualAndForecastedGraphVisible = esForm.IsActualAndForecastedGraphChecked;
esTabPage.IsActualVsPredictedGraphVisible = esForm.IsActualVsPredictedGraphChecked;
esTabPage.IsResidualGraphVisible = esForm.IsResidualGraphChecked;
esTabPage.IsResidualVsActualGraphVisible = esForm.IsResidualVsActualGraphChecked;
esTabPage.IsResidualVsPredictedGraphVisible = esForm.IsResidualVsPredictedGraphChecked;
esTabPage.DrawControl();
esTabPage.IsDrawn = true;
this.tabControlResult.AddTab(esTabPage);
this.tabControlResult.SelectedItem = esTabPage;
this.tabControlData.SelectedTab = this.tabPageResult;
}
}
}
public void Decomposition()
{
SelectAnalyzedVariable dlg = new SelectAnalyzedVariable(this.data.SeriesVariables);
dlg.ShowDialog();
if (dlg.DialogResult == DialogResult.OK)
{
DecompositionForm decForm = new DecompositionForm();
decForm.SetVariable(dlg.SelectedVariable);
decForm.ShowDialog();
if (decForm.DialogResult == DialogResult.Yes)
{
if (decForm.IsStoreTrend)
{
SeriesVariable var = new SeriesVariable(decForm.TrendName, "Trend Value of Decomposition Classic analysis of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - decForm.DecTable.trend.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < decForm.DecTable.trend.Length; i++) var[i + lag] = decForm.DecTable.trend[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (decForm.IsStoreDetrend)
{
SeriesVariable var = new SeriesVariable(decForm.DetrendName, "Detrended Value of Decomposition Classic analysis of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - decForm.DecTable.detrend.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < decForm.DecTable.detrend.Length; i++) var[i + lag] = decForm.DecTable.detrend[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (decForm.IsStoreDeseasonal)
{
SeriesVariable var = new SeriesVariable(decForm.DeseasonalName, "Seasonally Adj. Value of Decomposition Classic analysis of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - decForm.DecTable.deseasonal.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < decForm.DecTable.deseasonal.Length; i++) var[i + lag] = decForm.DecTable.deseasonal[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (decForm.IsStorePredicted)
{
SeriesVariable var = new SeriesVariable(decForm.PredictedName, "Predicted Value of Decomposition Classic analysis of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - decForm.DecTable.predicted.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < decForm.DecTable.predicted.Length; i++) var[i + lag] = decForm.DecTable.predicted[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
if (decForm.IsStoreResidual)
{
SeriesVariable var = new SeriesVariable(decForm.ResidualName, "Residual Value of Decomposition Classic analysis of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - decForm.DecTable.residual.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < decForm.DecTable.residual.Length; i++) var[i + lag] = decForm.DecTable.residual[i];
this.data.SeriesVariables.Add(var);
this.seriesDataList.Refresh();
}
DECResultTabPage decTabPage = new DECResultTabPage();
decTabPage.Title = "Decomposition : '" + dlg.SelectedVariable.VariableName + "'";
if (decForm.IsForecastedDataGridChecked)
{
decTabPage.SetData(this.data, dlg.SelectedVariable, decForm.DecProperties,
decForm.DecTable, decForm.Forecast(decForm.ForecastingStep));
}
else
{
decTabPage.SetData(this.data, dlg.SelectedVariable, decForm.DecProperties,
decForm.DecTable, null);
}
decTabPage.IsDecModelSummaryVisible = decForm.IsDecModelSummaryChecked;
decTabPage.IsDecompositionDataGridVisible = decForm.IsDecompositionDataGridChecked;
decTabPage.IsTrendVisible = decForm.IsTrendChecked;
decTabPage.IsDetrendVisible = decForm.IsDetrendChecked;
decTabPage.IsSeasonalVisible = decForm.IsSeasonalChecked;
decTabPage.IsDeseasonalVisible = decForm.IsDeseasonalChecked;
decTabPage.IsForecastedDataGridVisible = decForm.IsForecastedDataGridChecked;
decTabPage.IsActualPredictedAndTrendGraphVisible = decForm.IsActualPredictedAndTrendGraphChecked;
decTabPage.IsActualAndForecastedGraphVisible = decForm.IsActualAndForecastedGraphChecked;
decTabPage.IsActualVsPredictedGraphVisible = decForm.IsActualVsPredictedGraphChecked;
decTabPage.IsResidualGraphVisible = decForm.IsResidualGraphChecked;
decTabPage.IsResidualVsActualGraphVisible = decForm.IsResidualVsActualGraphChecked;
decTabPage.IsResidualVsPredictedGraphVisible = decForm.IsResidualVsPredictedGraphChecked;
decTabPage.IsDetrendGraphVisible = decForm.IsDetrendGraphChecked;
decTabPage.IsDeseasonalGraphVisible = decForm.IsDeseasonalGraphChecked;
decTabPage.DrawControl();
decTabPage.IsDrawn = true;
this.tabControlResult.AddTab(decTabPage);
this.tabControlResult.SelectedItem = decTabPage;
this.tabControlData.SelectedTab = this.tabPageResult;
}
}
}
private void cmdAddVariable_Click(object sender, EventArgs e)
{
SeriesVariables var = this.data.SeriesVariables;
zaitun.GUI.CreateSeriesVariable ser = new zaitun.GUI.CreateSeriesVariable();
ser.ShowDialog();
if (ser.DialogResult == DialogResult.OK)
{
try
{
SeriesVariable tmp = new SeriesVariable(ser.VariableName, ser.VariableDescription);
tmp.InitializeItem(this.data.NumberObservations);
var.Add(tmp);
this.seriesDataList.Refresh();
}
catch (DuplicateVariableException)
{
MessageBox.Show("Variable '" + ser.VariableName + "' already exist", "Duplicate Variable Name", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
}
public void TrendAnalysis()
{
SelectAnalyzedVariable dlg = new SelectAnalyzedVariable(this.data.SeriesVariables);
dlg.ShowDialog();
if (dlg.DialogResult == DialogResult.OK)
{
TrendAnalysisForm trendForm = new TrendAnalysisForm();
trendForm.SetVariable(dlg.SelectedVariable);
trendForm.ShowDialog();
if (trendForm.DialogResult == DialogResult.OK)
{
if (trendForm.IsStorePredicted)
{
SeriesVariable var = new SeriesVariable(trendForm.PredictedName, "Predicted Value of trend analysis of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - trendForm.Predicted.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < trendForm.Predicted.Length; i++) var[i + lag] = trendForm.Predicted[i];
try
{
this.data.SeriesVariables.Add(var);
}
catch (DuplicateVariableException)
{
MessageBox.Show("Variable '" + var.VariableName + "' already exist", "Duplicate Variable Name", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
this.seriesDataList.Refresh();
}
if (trendForm.IsStoreResidual)
{
SeriesVariable var = new SeriesVariable(trendForm.ResidualName, "Residual Value of trend analysis of variable '"
+ dlg.SelectedVariable.VariableName + "'");
int lag = dlg.SelectedVariable.SeriesValues.Count - trendForm.Residual.Length;
var.InitializeItem(dlg.SelectedVariable.SeriesValues.Count);
for (int i = 0; i < lag; i++) var[i] = double.NaN;
for (int i = 0; i < trendForm.Residual.Length; i++) var[i + lag] = trendForm.Residual[i];
try
{
this.data.SeriesVariables.Add(var);
}
catch (DuplicateVariableException)
{
MessageBox.Show("Variable '" + var.VariableName + "' already exist", "Duplicate Variable Name", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
this.seriesDataList.Refresh();
}
TrendAnalysisResultTabPage trendTabPage = new TrendAnalysisResultTabPage();
trendTabPage.Title = "Trend Analysis : '" + dlg.SelectedVariable.VariableName + "'";
if (trendForm.IsForecastedDataGridChecked)
{
trendTabPage.SetData(this.data, dlg.SelectedVariable, trendForm.TrendProperties, trendForm.Predicted,
trendForm.Forecast(trendForm.ForecastingStep), trendForm.Residual);
}
else
{
trendTabPage.SetData(this.data, dlg.SelectedVariable, trendForm.TrendProperties,
trendForm.Predicted, null, trendForm.Residual);
}
trendTabPage.IsTrendAnalysisModelSummaryVisible = trendForm.IsTrendAnalysisModelSummaryChecked;
trendTabPage.IsActualPredictedResidualDataGridVisible = trendForm.IsActualPredictedResidualDataGridChecked;
trendTabPage.IsForecastedDataGridVisible = trendForm.IsForecastedDataGridChecked;
trendTabPage.IsActualAndPredictedGraphVisible = trendForm.IsActualAndPredictedGraphChecked;
trendTabPage.IsActualAndForecastedGraphVisible = trendForm.IsActualAndForecastedGraphChecked;
trendTabPage.IsActualVsPredictedGraphVisible = trendForm.IsActualVsPredictedGraphChecked;
trendTabPage.IsResidualGraphVisible = trendForm.IsResidualGraphChecked;
trendTabPage.IsResidualVsActualGraphVisible = trendForm.IsResidualVsActualGraphChecked;
trendTabPage.IsResidualVsPredictedGraphVisible = trendForm.IsResidualVsPredictedGraphChecked;
trendTabPage.DrawControl();
trendTabPage.IsDrawn = true;
this.tabControlResult.AddTab(trendTabPage);
this.tabControlResult.SelectedItem = trendTabPage;
this.tabControlData.SelectedTab = this.tabPageResult;
}
}
}
/// <summary>
/// Predicate untuk pencarian variabel
/// </summary>
/// <param name="search">variabel yang akan dicari</param>
/// <returns>apakah nama variabel match</returns>
private static bool findVariablePredicate(SeriesVariable search)
{
return(search.VariableName.Equals(variableToFind.VariableName));
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="name">Stock Name</param>
/// <param name="description">Stock Description</param>
/// <param name="open">Stock's open variable</param>
/// <param name="close">Stock's close variable</param>
/// <param name="high">Stock's high variable</param>
/// <param name="low">Stock's low variable</param>
/// <param name="volume">Stock's volume variable</param>
public SeriesStock(string name, string description, SeriesVariable open, SeriesVariable close, SeriesVariable high, SeriesVariable low, SeriesVariable volume)
{
this.initialize(name, description, open, close, high, low, volume);
}
/// <summary>
/// Mengeset variabel yang aka dianalisis
/// </summary>
/// <param name="variable">variabel yang aka dianalisis</param>
public void SetVariable(SeriesVariable variable)
{
this.variable = variable;
this.data = variable.SeriesValuesNoNaN.ToArray();
double[,] actualSeries = new double[variable.Observations,2];
for (int i = 0; i < variable.Observations; i++)
{
actualSeries[i, 0] = i;
actualSeries[i, 1] = variable.SeriesValuesNoNaN[i];
}
seriesChart.RangeX = new DoubleRange(0, variable.Observations - 1);
seriesChart.UpdateDataSeries("Actual", actualSeries);
seriesChart.UpdateDataSeries("Predicted", null);
this.observationLabel.Text = "Observations : " + variable.Observations.ToString();
this.Text = "Neural Network Analysis Form : " + variable.VariableName;
}
/// <summary>
/// Membaca data
/// </summary>
/// <returns>Series Data hasil pembacaan</returns>
public SeriesData ReadData()
{
SeriesData tmpData;
seriesDataReader.ReadToFollowing("SeriesData");
seriesDataReader.ReadStartElement();
{
string seriesName = seriesDataReader.ReadElementContentAsString();
SeriesData.SeriesFrequency freq = (SeriesData.SeriesFrequency)seriesDataReader.ReadElementContentAsInt();
DateTime startDate = new DateTime();
DateTime endDate = new DateTime();
if (freq != SeriesData.SeriesFrequency.Undated)
{
startDate = XmlConvert.ToDateTime(seriesDataReader.ReadElementContentAsString(), "dd/MM/yyyy");
endDate = XmlConvert.ToDateTime(seriesDataReader.ReadElementContentAsString(), "dd/MM/yyyy");
}
else
{
seriesDataReader.Skip();
seriesDataReader.Skip();
}
int numberObservations = seriesDataReader.ReadElementContentAsInt();
if (freq != SeriesData.SeriesFrequency.Undated)
{
tmpData = new SeriesData(seriesName, freq, startDate, endDate);
}
else
{
tmpData = new SeriesData(seriesName, numberObservations);
}
}
seriesDataReader.ReadStartElement();
while (seriesDataReader.IsStartElement("SeriesVariable"))
{
SeriesVariable tmpVariable;
seriesDataReader.ReadStartElement();
{
string variableName = seriesDataReader.ReadElementContentAsString();
string variableDescription = seriesDataReader.ReadElementContentAsString();
tmpVariable = new SeriesVariable(variableName, variableDescription);
}
seriesDataReader.ReadStartElement();
for (int i = 0; i < tmpData.NumberObservations; i++)
{
tmpVariable.SeriesValues.Add(seriesDataReader.ReadElementContentAsDouble());
}
seriesDataReader.ReadEndElement();
tmpData.SeriesVariables.Add(tmpVariable);
seriesDataReader.ReadEndElement();
}
seriesDataReader.ReadToFollowing("SeriesGroups");
seriesDataReader.ReadStartElement();
while (seriesDataReader.IsStartElement("SeriesGroup"))
{
SeriesGroup tmpGroup;
SeriesVariables tmpGroupList = new SeriesVariables();
seriesDataReader.ReadStartElement();
string groupName = seriesDataReader.ReadElementContentAsString();
seriesDataReader.ReadStartElement();
while (seriesDataReader.IsStartElement("SeriesVariableItem"))
{
SeriesVariable tmpVariableItem;
string variableName = seriesDataReader.ReadElementContentAsString();
tmpVariableItem = tmpData.SeriesVariables[VariableFinder.FindVariableIndex(tmpData.SeriesVariables, variableName)];
tmpGroupList.Add(tmpVariableItem);
}
tmpGroup = new SeriesGroup(groupName, tmpGroupList);
seriesDataReader.ReadEndElement();
tmpData.SeriesGroups.Add(tmpGroup);
seriesDataReader.ReadEndElement();
}
return(tmpData);
}
/// <summary>
/// Mengeset variabel yang aka dianalisis
/// </summary>
/// <param name="variable">variabel yang aka dianalisis</param>
public void SetVariable(SeriesVariable variable)
{
this.variable = variable;
this.data = variable.SeriesValuesNoNaN.ToArray();
double[,] actualSeries = new double[variable.Observations,2];
this.actualDS.Length = variable.Observations;
for (int i = 0; i < variable.Observations; i++)
{
actualSeries[i, 0] = i;
actualSeries[i, 1] = variable.SeriesValuesNoNaN[i];
this.actualDS.Samples[i].x = i;
this.actualDS.Samples[i].y = (float)variable.SeriesValuesNoNaN[i];
}
//this.actualDS.AutoScaleY = true;
this.actualDS.YD0 = (float)variable.MinValue;
this.actualDS.YD1 = (float)variable.MaxValue;
this.predictedDS.YD0 = (float)variable.MinValue;
this.predictedDS.YD1 = (float)variable.MaxValue;
this.validationDS.YD0 = (float)variable.MinValue;
this.validationDS.YD1 = (float)variable.MaxValue;
this.seriesGraph.XD0 = 0;
this.seriesGraph.XD1 = variable.Observations;
this.seriesGraph.hasMovingGrid = false;
this.seriesGraph.hasBoundingBox = false;
this.seriesGraph.layout = PlotterGraphPaneEx.LayoutMode.NORMAL;
this.predictedDS.Active = false;
this.validationDS.Active = false;
this.seriesGraph.Refresh();
this.observationLabel.Text = "Observations : " + variable.Observations.ToString();
this.Text = "Neural Network Analysis Form : " + variable.VariableName;
}
