private void Forecast_Calc_Fill()
{//using WekaForcaster to culclate the predicted data for our current signal data.
//and show it in a chart for the user
try
{
// path to the signal data - found in the project folder ..WekaForecasting\bin\Debug
String pathToData = ".\\Signal.arff";
// load the signal data
Instances data = new Instances(new java.io.BufferedReader(new java.io.FileReader(pathToData)));
// new forecaster
WekaForecaster forecaster = new WekaForecaster();
// set the target we want to forecast - the attribute in our arff file.
forecaster.setFieldsToForecast("frequency");
// default underlying classifier is SMOreg (SVM) - we'll use
// MultilayerPerceptron for regression instead
forecaster.setBaseForecaster(new MultilayerPerceptron());
// set the attribute in our arff file that will be our time stamp.
forecaster.getTSLagMaker().setTimeStampField("time");
// set the lag creation to the data
forecaster.getTSLagMaker().setMinLag(1);
forecaster.getTSLagMaker().setMaxLag(returnBestMaxLagFit(forecaster, data));
// build the model
forecaster.buildForecaster(data);
// prime the forecaster with enough recent historical data
forecaster.primeForecaster(data);
//xFactor will help us to calculate the forecast data after the last data piont in the next_data
int xFactor = next_data.Length;
// forecast for the number of forecastUnits we set beyond the end of the training data
int forecastUnits = Convert.ToInt32(TextBox.Text) + xFactor;
double[] PredectedList = new double[forecastUnits];
var mylist = forecaster.forecast(forecastUnits);
//get the forcasted data and place it in the PredectedList
for (int i = 0; i < forecastUnits; i++)
{
String predict = mylist.get(i).ToString().Split(' ')[2];
PredectedList[i] = Convert.ToDouble(predict);
}
//lag represent our fixed lag between the time stamp of the predictions
double lag = forecaster.getTSLagMaker().getDeltaTime();
//DeltaTime will represent our changing lag between each time stamp at each prediction
double DeltaTime = 0;
//getting the last time stamp in our last point in the current data
double lastValidTime = data.lastInstance().value(0);
//clear Predicted_Data if alredy have data inside
Predicted_Data.Clear();
Negetive_Predicted_Data.Clear();
// output the predictions with their time stamp in the chart.
for (int i = 0; i < forecastUnits; i++)
{
if (i < next_data.Length - 1)
{//we still in the next data points and not in the predected points the user want to predict
Predicted_Data.Add(new ObservablePoint(next_data[i].Item1, PredectedList[i]));
//checks if the predected point is negetive if is negetive add the the black points series
if (PredectedList[i] < 0)
{
Negetive_Predicted_Data.Add(new ObservablePoint(next_data[i].Item1, PredectedList[i]));
}
//get the x from the last point in our next data to be the starting point in the predecred data
lastValidTime = next_data[i].Item1;
}
else
{//we are in the range of the predected points the user want to predict
//advance the current time to correspond to the forecasted values
DeltaTime = DeltaTime + lag;
//append the predicted data to the chart
Predicted_Data.Add(new ObservablePoint(lastValidTime + DeltaTime, PredectedList[i]));
//checks if the predected point is negetive if is negetive add the the black points series
if (PredectedList[i] < 0)
{
Negetive_Predicted_Data.Add(new ObservablePoint(lastValidTime + DeltaTime, PredectedList[i]));
}
}
}
//clear progressBar in the end of the progress
ProgressBar.Value = 0;
//checks if their wasn't negetive predected point and add the (0,0) point to the black points series
//for preserving the chart prespective
if (Negetive_Predicted_Data.Count == 0)
{
Negetive_Predicted_Data.Add(new ObservablePoint(0, 0));
}
}
catch (Exception ex) { ex.ToString(); }
}
private int returnBestMaxLagFit(WekaForecaster forecaster, Instances data)
{//return max lag that fits the best to the prediction
//calculate the best fit MaxLag for our predection
int bestFit = 1;
double sumOfDeferance = 100;
//Configure the ProgressBar
ProgressBar.Minimum = 0;
ProgressBar.Maximum = 100;
ProgressBar.Value = 0;
//Stores the value of the ProgressBar
double value = 0;
//Create a new instance of our ProgressBar Delegate that points
// to the ProgressBar's SetValue method.
UpdateProgressBarDelegate updatePbDelegate = new UpdateProgressBarDelegate(ProgressBar.SetValue);
//calculate the difference between the prediction points to the actual next data if the next culclation is smaller
//then befor replace the bestFit value with i;
for (int i = 1; i <= 24; i++)
{
if (ProgressBar.Value != ProgressBar.Maximum)
{
value += 4.16;
/*Update the Value of the ProgressBar:
* 1) Pass the "updatePbDelegate" delegate
* that points to the ProgressBar1.SetValue method
* 2) Set the DispatcherPriority to "Background"
* 3) Pass an Object() Array containing the property
* to update (ProgressBar.ValueProperty) and the new value */
Dispatcher.Invoke(updatePbDelegate,
System.Windows.Threading.DispatcherPriority.Background,
new object[] { ProgressBar.ValueProperty, value });
}
double sum = 0;
//define the max lag
forecaster.getTSLagMaker().setMaxLag(i);
// build the model
forecaster.buildForecaster(data);
// prime the forecaster with enough recent historical data
forecaster.primeForecaster(data);
//if we dont have at list 10 points in next data we will use all the next data points else we use only 10
int forecastUnits = 10;
if (next_data.Length < 10)
{
forecastUnits = next_data.Length;
}
// forecast for the number of forecastUnits we set beyond the end of the training data
var mylist = forecaster.forecast(forecastUnits);
//get the forcasted data and place it in the PredectedList
for (int j = 3; j < forecastUnits; j++)
{
String predict = mylist.get(j).ToString().Split(' ')[2];
sum = sum + Math.Abs(Convert.ToDouble(predict) - next_data[j - 2].Item2);
}
//calculate Standard Error
sum = sum / forecastUnits;
if (sum < sumOfDeferance)
{//save the smallest Standard Error
sumOfDeferance = sum;
bestFit = i;
}
}
//show Standard Error in the maim window
Standard_Error.Content = String.Format("Standard Error: {0:0.00000}", sumOfDeferance);
return(bestFit);
}
