/// <summary>
///
/// </summary>
/// <param name="args">
/// args[ 0 ] : timeSeriesFileName : The name of the file to load the time series from.
/// args[ 1 ] : forecastingSessionFileName : The name of the file to load the training session from.
/// args[ 2 ] : forecastingLogFileName : The name of the file to save the forecasting log into.
/// </param>
public static void Main(string[] args)
{
try
{
// Create the time series.
timeSeries = new TimeSeries(args[0], ' ');
// Read the forecasting session.
forecastingSession = new ForecastingSession(args[1]);
// The header of the forecasting log.
string[] forecastingLogHeader = new string[10] {
"Lags",
"Number of effective observations (n)",
"Number of hidden neurons",
"Number of parameters (p)",
"RSS (within-sample)",
"RSD (within-sample)",
"AIC",
"BIC",
"RSS (out-of-sample)",
"RSD (out-of-sample)"
};
// Write the forecasting log.
forecastingLog = new ForecastingLog(args[2], forecastingLogHeader);
while (!forecastingSession.EndOfStream)
{
Forecast(forecastingSession, forecastingLog);
}
}
catch
{
Console.WriteLine("Usage: MarketForecaster .timeseries .forecastingsession .forecastinglog");
}
finally
{
// Close the forecasting session.
if (forecastingSession != null)
{
forecastingSession.Close();
}
// Close the forecasting log.
if (forecastingLog != null)
{
forecastingLog.Close();
}
}
}
/// <summary>
///
/// </summary>
/// <param name="forecastingSession"></param>
/// <param name="forecastingLog"></param>
private static void Forecast(ForecastingSession forecastingSession, ForecastingLog forecastingLog)
{
// Step 0 : Read from the Forecasting Session
// ------------------------------------------
string[] words = forecastingSession.Read();
// The size of the test set.
string testSetSizeString = words[0].Trim();
int testSetSize = Int32.Parse(testSetSizeString);
// The lags.
string lagsString = words[1].Trim();
string[] lagStrings = lagsString.Split(',');
int[] lags = new int[lagStrings.Length];
for (int i = 0; i < lags.Length; i++)
{
lags[i] = Int32.Parse(lagStrings[i]);
}
// The leaps.
string leapsString = words[2].Trim();
string[] leapStrings = leapsString.Split(',');
int[] leaps = new int[leapStrings.Length];
for (int i = 0; i < leaps.Length; i++)
{
leaps[i] = Int32.Parse(leapStrings[i]);
}
// The numner of hidden neurons.
string hiddenNeuronCountString = words[3].Trim();
int hiddenNeuronCount = Int32.Parse(hiddenNeuronCountString);
// DEBUG : "Lags; Number of hidden neurons"
Console.WriteLine(lagsString + "; " + hiddenNeuronCountString);
// Step 1 : Alternative A : Building a training set (and a testing set) manually
// -----------------------------------------------------------------------------
// The training set.
TrainingSet trainingSet = timeSeries.BuildTrainingSet(lags, leaps);
// The testing set.
TrainingSet testSet = trainingSet.SeparateTestSet(trainingSet.Size - testSetSize, testSetSize);
// Step 2 : Building a blueprint of a network
// ------------------------------------------
// The input layer blueprint.
LayerBlueprint inputLayerBlueprint = new LayerBlueprint(lags.Length);
// The hidden layer blueprint.
ActivationLayerBlueprint hiddenlayerBlueprint = new ActivationLayerBlueprint(hiddenNeuronCount);
// The output layer blueprint.
ActivationLayerBlueprint outputLayerBlueprint = new ActivationLayerBlueprint(leaps.Length, new LinearActivationFunction());
// The network blueprint.
NetworkBlueprint networkBlueprint = new NetworkBlueprint(inputLayerBlueprint, hiddenlayerBlueprint, outputLayerBlueprint);
// Step 3 : Building a network
// ---------------------------
// The network.
Network network = new Network(networkBlueprint);
// Step 4 : Building a teacher
// ---------------------------
BackpropagationTeacher teacher = new BackpropagationTeacher(trainingSet, null, testSet);
// Step 5 : Training the network
// -----------------------------
int maxRunCount = 10;
int maxIterationCount = 10000;
double maxTolerableNetworkError = 0.0;
TrainingLog tl = teacher.Train(network, maxRunCount, maxIterationCount, maxTolerableNetworkError);
// Step 6 : Write into the Forecasting Log
// ---------------------------------------
words = new string[10] {
lagsString,
trainingSet.Size.ToString(),
hiddenNeuronCountString,
network.SynapseCount.ToString(),
tl.RSS_TrainingSet.ToString(),
tl.RSD_TrainingSet.ToString(),
tl.AIC.ToString(),
tl.BIC.ToString(),
tl.RSS_TestSet.ToString(),
tl.RSD_TestSet.ToString()
};
forecastingLog.Write(words);
// DEBUG : "RSS (within-sample); RSD (within-sample); AIC; BIC; RSS (out-of-sample); RSD (out-of-sample)"
Console.WriteLine(tl.RSS_TrainingSet.ToString() + "; " + tl.RSD_TrainingSet.ToString() + "; " + tl.AIC.ToString() + "; " + tl.BIC.ToString() + "; " + tl.RSS_TestSet.ToString() + "; " + tl.RSD_TestSet.ToString());
}
