public bool Forecast(int periodicityHint, List<double> data, int holdout, int range, string mode, double minValue, double maxValue, out IList<double> parameters,
out IList<IScalarDistribution> forecast, out IList<double> residuals)
{
bool retval = false;
parameters = null;
forecast = null;
residuals = null;
int c_maxIter = 2;
int fcastRange = 12;
AAdArapidEventDetector modelBuilder = new AAdArapidEventDetector(periodicityHint);
IList<EventImpactTrace> detectionHistory = null;
bool fOK = modelBuilder.EventDetectionLoop(data, 0, c_maxIter, false, out detectionHistory);
HoltWintersMultithreadedDimensionalityReduction forecaster = new HoltWintersMultithreadedDimensionalityReduction(periodicityHint);
retval = forecaster.SingleThreadForecast(data, 0, fcastRange, "NONPARAMETRIC", 0.0, double.MaxValue, out parameters, out forecast, out residuals);
return retval;
}
static void Main(string[] args)
{
string filename = Environment.GetEnvironmentVariable("TSF_NET_DIR") + "Sample_Periodic\\NorwichTemp2.csv";
Dictionary<string, List<double>> inputData = ParseCSVRows(filename);
ExcelApplicationWrapper excelApp = new ExcelApplicationWrapper();
excelApp.OpenExcel();
foreach (string q1 in inputData.Keys)
{
string strTab = q1 + ", trends";
if (strTab.Length > 30)
{
strTab = strTab.Substring(0, 30);
}
int c_maxIter = 2;
List<DateTime> timeline = null;
List<double> data = inputData[q1];
//Done extracting and aggregating data for query q1
int periodicityHint = 12;
//Step1. Detect deviant patterns and outlying spikes
//A slower much more precise model builder commented out here
//AAdAdrWithShocks modelBuilder = new AAdAdrWithShocks(periodicityHint, 0.1, (List<int>) null, null);
//modelBuilder.InterferenceLag = 12;
AAdArapidEventDetector modelBuilder = new AAdArapidEventDetector(periodicityHint);
IList<EventImpactTrace> detectionHistory = null;
bool fOK = modelBuilder.EventDetectionLoop(data, 0, c_maxIter, false,
out detectionHistory);
int fcastRange = 12;
IList<IScalarDistribution> fullForecast = null;
HoltWintersMultithreadedDimensionalityReduction forecaster =
new HoltWintersMultithreadedDimensionalityReduction(periodicityHint);
IList<double> paramtrs = null;
IList<double> residuals = null;
try
{
//Example of "full service" forecast, with expected values and predict distributions
forecaster.SingleThreadForecast(data, 0, fcastRange, "NONPARAMETRIC", 0.0, double.MaxValue,
out paramtrs, out fullForecast, out residuals);
}
catch (Exception outerException)
{
}
//Preparing for ... and using Excel middleware to generate Excel chart programmatically
Dictionary<Timestamp, double> dictSigmas = null;
Dictionary<Timestamp, double> dictMeans = null;
List<Timestamp> timeIndex = null;
MakeForecastOnlyDictionaries
(data.Count, fullForecast, out timeIndex, out dictMeans, out dictSigmas);
//Cosmetics tweak
dictMeans.Add(new Timestamp(data.Count - 1), data.Last());
AddExcelChart(excelApp, detectionHistory.Last(), q1,
MakeDataDictionary(timeIndex, data), dictMeans, dictSigmas);
}
//excelApp.CloseExcel(); //Uncomment to get the workbook closed automatically
}