/// <summary>
/// Uses forecast equation for each historical year.
/// </summary>
/// <param name="year1"></param>
/// <param name="year2"></param>
/// <returns>a table comparing acutal to forecasted values</returns>
public DataTable RunHistoricalForecasts(int year1, int year2, bool lookAhead, int forecastMonth, double[] estimationFactors, int forecastDay = 1)
{
DataTable tbl = new DataTable(Name);
//Logger.EnableLogger();
tbl.Columns.Add("Notes");
tbl.Columns.Add("Year");
tbl.Columns.Add("Actual Forecast Period Volume", typeof(double));
tbl.Columns.Add("Forecasted Period Volume", typeof(double));
tbl.Columns.Add("Difference", typeof(double));
tbl.Columns.Add("Error", typeof(double));
tbl.Columns.Add("Actual Residual Volume", typeof(double));
tbl.Columns.Add("Forecasted Residual Volume", typeof(double));
tbl.Columns.Add("Difference2", typeof(double));
tbl.Columns.Add("Error2", typeof(double));
// [JR] Hacks to write the inputs to the MLR process
foreach (var item in this.YTerm.MonthNames)
{
tbl.Columns.Add("Y-Runoff-" + item.ToUpper(), typeof(double));
}
for (int i = 0; i < this.XTerms.Count(); i++)
{
var ithTerm = this.XTerms[i];
var termName = "X" + ithTerm.Number + "-" + ithTerm.ForecastTermType;
foreach (var item in ithTerm.MonthNames)
{
tbl.Columns.Add(termName + "-" + item.ToUpper(), typeof(double));
}
}
for (int i = 1; i < estimationFactors.Length; i++)
{
tbl.Columns.Add("Forecast " + estimationFactors[i], typeof(double));
//tbl.Columns.Add("Difference " + estimationFactors[i], typeof(double));
//tbl.Columns.Add("Error " + estimationFactors[i], typeof(double));
}
HydrometData.SetupHydrometData(StartYear, EndYear, GetCbttPcodeList().ToArray(), DateTime.MaxValue, true, true); // date is ignored when reading all years
string forecastPeriod = "";
for (int year = year1; year <= year2; year++)
{
var row = tbl.NewRow();
row["Year"] = year;
Logger.WriteLine(year.ToString(), "ui");
DateTime t = new DateTime(year, forecastMonth, forecastDay);
try
{
var fcResult = Evaluate(t, lookAhead, estimationFactors[0], false);
var actual = YTerm.SeasonalRunoff(t);
row["Actual Forecast Period Volume"] = actual;
row["Forecasted Period Volume"] = fcResult.Forecast;
var diff = fcResult.Forecast - actual; // convention of FORTRAN
row["Difference"] = diff;
row["Error"] = (diff / actual) * 100.0;
var actualResid = actual - YTerm.RunoffToDate(t);
//var forecastResid = fcResult.GetForecastForSummary(lookAhead);
var forecastResid = fcResult.Forecast - YTerm.RunoffToDate(t);
row["Actual Residual Volume"] = actualResid;
row["Forecasted Residual Volume"] = forecastResid;
row["Difference2"] = forecastResid - actualResid;
row["Error2"] = (forecastResid - actualResid) / actualResid * 100.0;
for (int i = 1; i < estimationFactors.Length; i++)
{
var s = " " + estimationFactors[i];
fcResult = Evaluate(t, lookAhead, estimationFactors[i], false);
row["Forecasted Period Volume" + s] = fcResult.Forecast;
diff = actual - fcResult.Forecast; // convention of FORTRAN
//row["Difference"+s] = diff;
//row["Error"+s] = (diff / actual) * 100.0;
}
forecastPeriod = fcResult.ForecastPeriod;
// [JR] Hacks to write the inputs to the MLR process
#region
var sTEMP = this.YTerm.yData;
for (int ithMon = 0; ithMon < sTEMP.Count(); ithMon++)
{
row["Y-Runoff-" + sTEMP[ithMon].DateTime.ToString("MMM").ToUpper()] = sTEMP[ithMon].Value;
}
foreach (var term in this.XTerms)
{
if (term.xData.Count() > 0)
{
var termName = "X" + term.Number + "-" + term.ForecastTermType;
var sList = term.xData;
var sSum = new Reclamation.TimeSeries.Series();
for (int i = 0; i < sList.Count; i++)
{
foreach (var point in sList[i])
{
if (i == 0)
{
sSum.Add(point.DateTime, 0.0);
}
sSum[point.DateTime] = new Reclamation.TimeSeries.Point(point.DateTime, sSum[point.DateTime].Value + point.Value);
}
}
foreach (var point in sSum)
{
try
{
row[termName + "-" + point.DateTime.ToString("MMM").ToUpper()] = point.Value;
}
catch
{
}
}
}
}
#endregion
}
catch (Exception ex)
{
row["Notes"] = ex.Message;
for (int i = 2; i < row.ItemArray.Count(); i++)
{
row[i] = Double.NaN;
}
}
tbl.Rows.Add(row);
}
// add some notes
if (tbl.Rows.Count >= 5)
{
tbl.Rows[0]["Notes"] = this.Name;
tbl.Rows[1]["Notes"] = "Current Forecast Month: " + forecastMonth;
tbl.Rows[2]["Notes"] = "Current Forecast Day: " + forecastDay;
tbl.Rows[3]["Notes"] = "Current Forecast Period: " + forecastPeriod;
tbl.Rows[4]["Notes"] = "Full Forecast Period: " + this.YTerm.MonthNames[0].ToString().ToUpper() +
"-" + this.YTerm.MonthNames[this.YTerm.MonthNames.Count() - 1].ToString().ToUpper();
}
var summaryRow = tbl.NewRow();
summaryRow["Year"] = "Average";
//summaryRow["Actual Forecast Period Volume"] = tbl.AsEnumerable().Average(x => x.Field<double>("Actual Forecast Period Volume"));
//summaryRow["Forecasted Period Volume"] = tbl.AsEnumerable().Average(x => x.Field<double>("Forecasted Period Volume"));
//summaryRow["Difference"] = tbl.AsEnumerable().Average(x => x.Field<double>("Difference"));
//summaryRow["Error"] = tbl.AsEnumerable().Average(x => x.Field<double>("Error"));
//summaryRow["Actual Residual Volume"] = tbl.AsEnumerable().Average(x => x.Field<double>("Actual Residual Volume"));
//summaryRow["Forecasted Residual Volume"] = tbl.AsEnumerable().Average(x => x.Field<double>("Forecasted Residual Volume"));
//summaryRow["Difference2"] = tbl.AsEnumerable().Average(x => x.Field<double>("Difference2"));
//summaryRow["Error2"] = tbl.AsEnumerable().Average(x => x.Field<double>("Error2"));
for (int i = 2; i < tbl.Columns.Count; i++)
{
var colName = tbl.Columns[i].ColumnName;
try
{
var colAvg = tbl.AsEnumerable().Select(x => x.Field <double>(colName)).Where(s => s.ToString() != Double.NaN.ToString()).Average();
summaryRow[colName] = colAvg;
}
catch
{
}
}
// Get percent of average for MLR inputs
for (int i = 10; i < tbl.Columns.Count; i++)
{
var colName = tbl.Columns[i].ColumnName;
try
{
DataColumn ithColumn = tbl.Columns[colName];
foreach (DataRow row in tbl.Rows)
{
double oldVal = row.Field <double>(ithColumn);
row.SetField(ithColumn, oldVal / Convert.ToDouble(summaryRow[colName].ToString()));
}
}
catch
{
}
}
tbl.Rows.Add(summaryRow);
return(tbl);
}
public ForecastResult Evaluate(DateTime t, bool lookAhead, double estimationScaleFactor, bool setupCache = true)
{
Logger.WriteLine("Evaluate( " + this.Name + ")");
var rval = new ForecastResult();
rval.Equation = this;
rval.EstimationFactor = estimationScaleFactor;
string msg = t.ToShortDateString() + " Forecast " + this.Name + " " + DateTime.Now.ToShortDateString();
rval.Details.Add(msg);
Logger.WriteLine(msg);
if (setupCache)
{
HydrometData.SetupHydrometData(StartYear, EndYear, GetCbttPcodeList().ToArray(), t, true, false);
}
rval.Forecast = 0.0;
//var total = 0;
int i = 0;
for (i = 0; i < this.XTerms.Count; i++)
{
var X = XTerms[i];
Logger.WriteLine("Evaluating X" + X.Number);
var d = X.Evaluate(t, lookAhead, estimationScaleFactor);
rval.Forecast += d * this.coefficients[X.Number - 1];// apply coeficients...
rval.Details.AddRange(X.Details());
rval.Details.Add("X" + X.Number + " = " + d.ToString("F2"));
rval.Details.Add("");
Logger.WriteLine("X" + X.Number + "= " + rval.Forecast);
}
rval.Details.Add("");
rval.Forecast += coefficients[coefficients.Length - 1];// k term
// Y = C
string s = "Coefficients: ";
for (i = 0; i < coefficients.Length; i++)
{
s += " " + coefficients[i].ToString("F4");
}
rval.Details.Add(s);
//var runoff = YTerm.Evaluate(t, lookAhead,estimationScaleFactor);
var runoffToDate = YTerm.RunoffToDate(t);
var seasonalRunoff = YTerm.SeasonalRunoff(t);
rval.Details.AddRange(YTerm.Details());
var dr = YTerm.MonthNames;
rval.Details.Add("");
rval.ForecastPeriod = (t.ToString("MMM") + "-" + dr[dr.Count - 1]).ToUpper();
rval.AverageRunoff = Lookup30YearAverageRunoff(t);
string fullPeriod = (dr[0] + "-" + dr[dr.Count - 1]).ToUpper();
rval.Details.Add(fullPeriod + " = " + rval.Forecast.ToString("F2"));
if (lookAhead)
{
// actual runoff
rval.Details.Add("++ using look ahead for future data");
rval.Details.Add(dr[0] + " - " + dr[dr.Count - 1] + " runoff : " + seasonalRunoff.ToString("F1"));
}
else
{
rval.Details.Add("Runoff to Date : " + runoffToDate.ToString("F1"));
// forecast forward
var fc = rval.Forecast - runoffToDate;
rval.ResidualForecast = fc;
rval.Details.Add("Date - " + dr[dr.Count - 1] + " forecast : " + fc.ToString("F1"));
}
rval.Details.Add("average " + rval.ForecastPeriod + " runoff = " + rval.AverageRunoff.ToString("F2"));
return(rval);
}
/// <summary>
/// Uses forecast equation for each historical year.
/// </summary>
/// <param name="year1"></param>
/// <param name="year2"></param>
/// <returns>a table comparing acutal to forecasted values</returns>
public DataTable RunHistoricalForecasts(int year1, int year2, bool lookAhead, int forecastMonth, double[] estimationFactors, int forecastDay = 1)
{
DataTable tbl = new DataTable(Name);
//Logger.EnableLogger();
tbl.Columns.Add("Notes");
tbl.Columns.Add("Year");
tbl.Columns.Add("Actual Forecast Period Volume", typeof(double));
tbl.Columns.Add("Forecasted Period Volume", typeof(double));
tbl.Columns.Add("Difference", typeof(double));
tbl.Columns.Add("Error", typeof(double));
tbl.Columns.Add("Actual Residual Volume", typeof(double));
tbl.Columns.Add("Forecasted Residual Volume", typeof(double));
tbl.Columns.Add("Difference2", typeof(double));
tbl.Columns.Add("Error2", typeof(double));
for (int i = 1; i < estimationFactors.Length; i++)
{
tbl.Columns.Add("Forecast " + estimationFactors[i], typeof(double));
//tbl.Columns.Add("Difference " + estimationFactors[i], typeof(double));
//tbl.Columns.Add("Error " + estimationFactors[i], typeof(double));
}
HydrometData.SetupHydrometData(StartYear, EndYear, GetCbttPcodeList().ToArray(), DateTime.MaxValue, true, true); // date is ignored when reading all years
for (int year = year1; year <= year2; year++)
{
var row = tbl.NewRow();
row["Year"] = year;
Logger.WriteLine(year.ToString(), "ui");
DateTime t = new DateTime(year, forecastMonth, forecastDay);
try
{
var fcResult = Evaluate(t, lookAhead, estimationFactors[0], false);
var actual = YTerm.SeasonalRunoff(t);
row["Actual Forecast Period Volume"] = actual;
row["Forecasted Period Volume"] = fcResult.Forecast;
var diff = fcResult.Forecast - actual; // convention of FORTRAN
row["Difference"] = diff;
row["Error"] = (diff / actual) * 100.0;
var actualResid = actual - YTerm.RunoffToDate(t);
var forecastResid = fcResult.GetForecastForSummary(lookAhead);
row["Actual Residual Volume"] = actualResid;
row["Forecasted Residual Volume"] = forecastResid;
row["Difference2"] = forecastResid - actualResid;
row["Error2"] = (forecastResid - actualResid) / actualResid * 100.0;
for (int i = 1; i < estimationFactors.Length; i++)
{
var s = " " + estimationFactors[i];
fcResult = Evaluate(t, lookAhead, estimationFactors[i], false);
row["Forecasted Period Volume" + s] = fcResult.Forecast;
diff = actual - fcResult.Forecast; // convention of FORTRAN
//row["Difference"+s] = diff;
//row["Error"+s] = (diff / actual) * 100.0;
}
}
catch (Exception ex)
{
row["Notes"] = ex.Message;
}
tbl.Rows.Add(row);
}
// add some notes
if (tbl.Rows.Count >= 3)
{
tbl.Rows[0]["Notes"] = this.Name;
tbl.Rows[1]["Notes"] = "forecast Month " + forecastMonth;
tbl.Rows[2]["Notes"] = "forecast Day " + forecastDay;
}
var summaryRow = tbl.NewRow();
summaryRow["Year"] = "Average";
summaryRow["Actual Forecast Period Volume"] = tbl.AsEnumerable().Average(x => x.Field <double>("Actual Forecast Period Volume"));
summaryRow["Forecasted Period Volume"] = tbl.AsEnumerable().Average(x => x.Field <double>("Forecasted Period Volume"));
summaryRow["Difference"] = tbl.AsEnumerable().Average(x => x.Field <double>("Difference"));
summaryRow["Error"] = tbl.AsEnumerable().Average(x => x.Field <double>("Error"));
summaryRow["Actual Residual Volume"] = tbl.AsEnumerable().Average(x => x.Field <double>("Actual Residual Volume"));
summaryRow["Forecasted Residual Volume"] = tbl.AsEnumerable().Average(x => x.Field <double>("Forecasted Residual Volume"));
summaryRow["Difference2"] = tbl.AsEnumerable().Average(x => x.Field <double>("Difference2"));
summaryRow["Error2"] = tbl.AsEnumerable().Average(x => x.Field <double>("Error2"));
tbl.Rows.Add(summaryRow);
return(tbl);
}