private void CmdWMA_Click(object sender, EventArgs e)
{
ForecastTable dt = TimeSeries.weightedMovingAverage(GetInput().ToArray(), 5, (Decimal)0.05, (Decimal)0.15, (Decimal)0.8);
grdResults.DataSource = dt;
updateMeasurements(dt);
}
private void CmdARS_Click(object sender, EventArgs e)
{
ForecastTable dt = TimeSeries.adaptiveRateSmoothing(GetInput().ToArray(), 5, (Decimal)0.2, (Decimal)0.8);
grdResults.DataSource = dt;
updateMeasurements(dt);
}
private void CmdNaive_Click(object sender, EventArgs e)
{
ForecastTable dt = TimeSeries.naive(GetInput().ToArray(), 5, 0);
grdResults.DataSource = dt;
updateMeasurements(dt);
}
private void CmdSMA_Click(object sender, EventArgs e)
{
ForecastTable dt = TimeSeries.simpleMovingAverage(GetInput().ToArray(), 5, 3, 0);
grdResults.DataSource = dt;
updateMeasurements(dt);
}
//
// Adaptive Rate Smoothing
//
public static ForecastTable adaptiveRateSmoothing(decimal[] values, int Extension, decimal MinGamma, decimal MaxGamma)
{
ForecastTable dt = new ForecastTable();
for (Int32 i = 0; i < (values.Length + Extension); i++)
{
//Insert a row for each value in set
DataRow row = dt.NewRow();
dt.Rows.Add(row);
row.BeginEdit();
//assign its sequence number
row["Instance"] = i;
if (i < values.Length)
{
row["Value"] = values[i];
if (i == 0)
{//initialize first row
row["Forecast"] = values[i];
}
else
{//calculate gamma and forecast value
DataRow priorRow = dt.Select("Instance=" + (i - 1).ToString())[0];
decimal PriorForecast = (Decimal)priorRow["Forecast"];
decimal PriorValue = (Decimal)priorRow["Value"];
//decimal Gamma = Math.Abs(TrackingSignal(dt, 3));
//if (Gamma < MinGamma)
// Gamma = MinGamma;
//if (Gamma > MaxGamma)
// Gamma = MaxGamma;
//row["Forecast"] = PriorForecast + (Gamma * (PriorValue - PriorForecast));
}
}
else
{//extension set, can't use actual values anymore
DataRow priorRow = dt.Select("Instance=" + (i - 1).ToString())[0];
decimal PriorForecast = (Decimal)priorRow["Forecast"];
decimal PriorValue = (Decimal)priorRow["Forecast"];
//decimal Gamma = Math.Abs(TrackingSignal(dt, 3));
//if (Gamma < MinGamma)
// Gamma = MinGamma;
//if (Gamma > MaxGamma)
// Gamma = MaxGamma;
//row["Forecast"] = PriorForecast + (Gamma * (PriorValue - PriorForecast));
}
row.EndEdit();
}
dt.AcceptChanges();
return(dt);
}
//CumulativeSignedError = Sum( E(t) )
public static decimal CumulativeSignedError(ForecastTable dt, int IgnoreInitial)
{
string Filter = "Error Is Not Null AND Instance > " + IgnoreInitial.ToString();
if (dt.Select(Filter).Length == 0)
{
return(0);
}
return((Decimal)dt.Compute("SUM(Error)", Filter));
}
//MeanAbsolutePercentError = Sum( |PercentError| ) / n
public static decimal MeanAbsolutePercentError(ForecastTable dt, int IgnoreInitial)
{
string Filter = "AbsolutePercentError Is Not Null AND Instance > " + IgnoreInitial.ToString();
if (dt.Select(Filter).Length == 0)
{
return(1);
}
return((Decimal)dt.Compute("AVG(AbsolutePercentError)", Filter));
}
//Tracking signal = MeanSignedError / MeanAbsoluteError
public static decimal TrackingSignal(ForecastTable dt, bool Holdout, int IgnoreInitial)
{
decimal MAE = MeanAbsoluteError(dt, Holdout, IgnoreInitial);
if (MAE == 0)
{
return(0);
}
return(MeanSignedError(dt, Holdout, IgnoreInitial) / MAE);
}
private void updateMeasurements(ForecastTable dt)
{
if (dt == null)
{
return;
}
lblMeanSignedDeviaiton.Text = "MSD: " + TimeSeries.MeanSignedError(dt, false, TimeSeries.DEFAULT_IGNORE).ToString();
lblMeanAbsoluteDeviation.Text = "MAD: " + TimeSeries.MeanAbsoluteError(dt, false, TimeSeries.DEFAULT_IGNORE).ToString();
lblMeanAbsolutePercentError.Text = "MAPE: " + TimeSeries.MeanAbsolutePercentError(dt, false, TimeSeries.DEFAULT_IGNORE).ToString();
lblMeanPercentError.Text = "MPE: " + TimeSeries.MeanPercentError(dt, false, TimeSeries.DEFAULT_IGNORE).ToString();
lblMeanSquaredError.Text = "MSE: " + TimeSeries.MeanSquaredError(dt, false, TimeSeries.DEFAULT_IGNORE, 0).ToString();
lblTrackingSignal.Text = "TS: " + TimeSeries.TrackingSignal(dt, false, TimeSeries.DEFAULT_IGNORE).ToString();
}
//CumulativeAbsoluteError = Sum( |E(t)| )
public static decimal CumulativeAbsoluteError(ForecastTable dt, bool Holdout, int IgnoreInitial)
{
string Filter = "AbsoluteError Is Not Null AND Instance > " + IgnoreInitial.ToString();
if (Holdout)
{
Filter += " AND Holdout=True";
}
if (dt.Select(Filter).Length == 0)
{
return(0);
}
return((Decimal)dt.Compute("SUM(AbsoluteError)", Filter));
}
//
//Exponential Smoothing
//
// F(t+1) = ( Alpha * D(t) ) + (1 - Alpha) * F(t)
//
public static ForecastTable exponentialSmoothing(decimal[] values, int Extension, decimal Alpha)
{
ForecastTable dt = new ForecastTable();
for (Int32 i = 0; i < (values.Length + Extension); i++)
{
//Insert a row for each value in set
DataRow row = dt.NewRow();
dt.Rows.Add(row);
row.BeginEdit();
//assign its sequence number
row["Instance"] = i;
if (i < values.Length)
{//test set
row["Value"] = values[i];
if (i == 0)
{//initialize first value
row["Forecast"] = values[i];
}
else
{
//main area of forecasting
DataRow priorRow = dt.Select("Instance=" + (i - 1).ToString())[0];
decimal PriorForecast = (Decimal)priorRow["Forecast"];
decimal PriorValue = (Decimal)priorRow["Value"];
row["Forecast"] = PriorForecast + (Alpha * (PriorValue - PriorForecast));
}
}
else
{//extension has to use prior forecast instead of prior value
DataRow priorRow = dt.Select("Instance=" + (i - 1).ToString())[0];
decimal PriorForecast = (Decimal)priorRow["Forecast"];
decimal PriorValue = (Decimal)priorRow["Forecast"];
row["Forecast"] = PriorForecast + (Alpha * (PriorValue - PriorForecast));
}
row.EndEdit();
}
dt.AcceptChanges();
return(dt);
}
//MSE = Sum( E(t)^2 ) / n
public static decimal MeanSquaredError(ForecastTable dt, int IgnoreInitial, int DegreesOfFreedom)
{
decimal SquareError = 0;
string Filter = "Error Is Not Null AND Instance > " + IgnoreInitial.ToString();
DataRow[] rows = dt.Select(Filter);
if (rows.Length == 0)
{
return(0);
}
foreach (DataRow row in rows)
{
SquareError = (Decimal)Math.Pow(Double.Parse(row["Error"].ToString()), (Double)2.0);
}
return(SquareError / (dt.Rows.Count - DegreesOfFreedom));
}
//Bayes: use prior actual value as forecast
public static ForecastTable naive(decimal[] values, int Extension, int Holdout)
{
ForecastTable dt = new ForecastTable();
for (Int32 i = 0; i < (values.Length + Extension); i++)
{
//Insert a row for each value in set
DataRow row = dt.NewRow();
dt.Rows.Add(row);
row.BeginEdit();
//assign its sequence number
row["Instance"] = i;
//if i is in the holdout range of values
//row["Holdout"] = (i > (values.Length - 1 - Holdout)) && (i < values.Length);
if (i < values.Length)
{ //processing values which actually occurred
//Assign the actual value to the DataRow
row["Value"] = values[i];
if (i == 0)
{
//first row, value gets itself
row["Forecast"] = values[i];
}
else
{
//Put the prior row's value into the current row's forecasted value
row["Forecast"] = values[i - 1];
}
}
else
{//Extension rows
row["Forecast"] = values[values.Length - 1];
}
row.EndEdit();
}
dt.AcceptChanges();
return(dt);
}
//
//Simple Moving Average
//
// ( Dt + D(t-1) + D(t-2) + ... + D(t-n+1) )
// F(t+1) = -----------------------------------------
// n
public static ForecastTable simpleMovingAverage(decimal[] values, int Extension, int Periods, int Holdout)
{
ForecastTable dt = new ForecastTable();
for (Int32 i = 0; i < values.Length + Extension; i++)
{
//Insert a row for each value in set
DataRow row = dt.NewRow();
dt.Rows.Add(row);
row.BeginEdit();
//assign its sequence number
row["Instance"] = i;
if (i < values.Length)
{//processing values which actually occurred
row["Value"] = values[i];
}
//Indicate if this is a holdout row
if (i == 0)
{//Initialize first row with its own value
row["Forecast"] = values[i];
}
else if (i <= values.Length - Holdout)
{//processing values which actually occurred, but not in holdout set
decimal avg = 0;
DataRow[] rows = dt.Select("Instance>=" + (i - Periods).ToString() + " AND Instance < " + i.ToString(), "Instance");
foreach (DataRow priorRow in rows)
{
avg += (Decimal)priorRow["Value"];
}
avg /= rows.Length;
row["Forecast"] = avg;
}
else
{//must be in the holdout set or the extension
decimal avg = 0;
//get the Periods-prior rows and calculate an average actual value
DataRow[] rows = dt.Select("Instance>=" + (i - Periods).ToString() + " AND Instance < " + i.ToString(), "Instance");
foreach (DataRow priorRow in rows)
{
if ((Int32)priorRow["Instance"] < values.Length)
{//in the test or holdout set
avg += (Decimal)priorRow["Value"];
}
else
{//extension, use forecast since we don't have an actual value
avg += (Decimal)priorRow["Forecast"];
}
}
avg /= rows.Length;
//set the forecasted value
row["Forecast"] = avg;
}
row.EndEdit();
}
dt.AcceptChanges();
return(dt);
}
//
//Weighted Moving Average
//
// F(t+1) = (Weight1 * D(t)) + (Weight2 * D(t-1)) + (Weight3 * D(t-2)) + ... + (WeightN * D(t-n+1))
//
public static ForecastTable weightedMovingAverage(decimal[] values, int Extension, params decimal[] PeriodWeight)
{
//PeriodWeight[].Length is used to determine the number of periods over which to average
//PeriodWeight[x] is used to apply a weight to the prior period's value
//Make sure PeriodWeight values add up to 100%
decimal test = 0;
foreach (decimal weight in PeriodWeight)
{
test += weight;
}
if (test != 1)
{
return(null);
}
ForecastTable dt = new ForecastTable();
for (Int32 i = 0; i < values.Length + Extension; i++)
{
//Insert a row for each value in set
DataRow row = dt.NewRow();
dt.Rows.Add(row);
row.BeginEdit();
//assign its sequence number
row["Instance"] = i;
if (i < values.Length)
{//we're in the test set
row["Value"] = values[i];
}
if (i == 0)
{//initialize forecast with first row's value
row["Forecast"] = values[i];
}
else if ((i < values.Length) && (i < PeriodWeight.Length))
{//processing one of the first rows, before we've advanced enough to properly weight past rows
decimal avg = 0;
//Get the datarows representing the values within the WMA length
DataRow[] rows = dt.Select("Instance>=" + (i - PeriodWeight.Length).ToString() + " AND Instance < " + i.ToString(), "Instance");
for (int j = 0; j < rows.Length; j++)
{//apply an initial, uniform weight (1 / rows.Length) to the initial rows
avg += (Decimal)rows[j]["Value"] * (1 / rows.Length);
}
row["Forecast"] = avg;
}
else if ((i < values.Length) && (i >= PeriodWeight.Length))
{//Out of initial rows and processing the test set
decimal avg = 0;
//Get the rows within the weight range just prior to the current row
DataRow[] rows = dt.Select("Instance>=" + (i - PeriodWeight.Length).ToString() + " AND Instance < " + i.ToString(), "Instance");
for (int j = 0; j <= rows.Length - 1; j++)
{//Apply the appropriate period's weight to the value
avg += (Decimal)rows[j]["Value"] * PeriodWeight[j];
}
//Assign the forecasted value to the current row
row["Forecast"] = avg;
}
else
{//into the extension
decimal avg = 0;
DataRow[] rows = dt.Select("Instance>=" + (i - PeriodWeight.Length).ToString() + " AND Instance < " + i.ToString(), "Instance");
for (int j = 0; j < rows.Length; j++)
{//with no actual values to weight, use the previous rows' forecast instead
avg += (Decimal)rows[j]["Forecast"] * PeriodWeight[j];
}
row["Forecast"] = avg;
}
row.EndEdit();
}
dt.AcceptChanges();
return(dt);
}
protected void Button1_Click(object sender, EventArgs e)
{
//count total transcation of area for reason
con.Open();
SqlCommand da1 = new SqlCommand("SELECT top 15 Temperature FROM [dataset] where name='" + DropDownList1.Text + "'", con);
SqlDataReader dt1 = da1.ExecuteReader();
if (dt1.HasRows)
{
GridView1.DataSource = dt1;
GridView1.DataBind();
}
con.Close();
con.Open();
SqlCommand da = new SqlCommand("select top 5 Temperature from [dataset] where [name]='" + DropDownList1.Text + "' ", con);
SqlDataReader dr = da.ExecuteReader();
while (dr.Read())
{
string productname = dr[0].ToString();
top += productname + ",";
txtvalue.Text = top.TrimEnd(',');
}
txtvalue.Text.TrimEnd(',');
con.Close();
DataTable dt = new DataTable();
con.Open();
SqlCommand cmd1 = new SqlCommand("select top 5 name,Temperature from [dataset] where [name]='" + DropDownList1.Text + "' ", con);
SqlDataAdapter sda = new SqlDataAdapter(cmd1);
sda.Fill(dt);
con.Close();
string[] x = new string[dt.Rows.Count];
int[] y = new int[dt.Rows.Count];
for (int i = 0; i < dt.Rows.Count; i++)
{
x[i] = dt.Rows[i][0].ToString();
y[i] = Convert.ToInt32(dt.Rows[i][1]);
}
Chart1.Series[0].Points.DataBindXY(x, y);
ForecastTable dt10 = TimeSeries.naive(GetInputforshirt(), 1, 6);
GridView2.DataSource = dt10;
GridView2.DataBind();
ForecastTable dt11 = TimeSeries.weightedMovingAverage(GetInputforshirt(), 1, (Decimal)0.05, (Decimal)0.15, (Decimal)0.8);
GridView3.DataSource = dt11;
GridView3.DataBind();
ForecastTable dt12 = TimeSeries.wellsweilderMovingaverage(GetInputforshirt(), 1, 2, 2);
GridView6.DataSource = dt12;
GridView6.DataBind();
con.Open();
SqlCommand da12 = new SqlCommand("select top 5 PulseRate from [dataset] where [name]='" + DropDownList1.Text + "' ", con);
SqlDataReader dr1 = da12.ExecuteReader();
while (dr1.Read())
{
string productname = dr1[0].ToString();
top1 += productname + ",";
txtheartbeat.Text = top1.TrimEnd(',');
}
txtheartbeat.Text.TrimEnd(',');
con.Close();
DataTable dt2 = new DataTable();
con.Open();
SqlCommand cmd2 = new SqlCommand("select top 5 name,PulseRate from [dataset] where [name]='" + DropDownList1.Text + "' ", con);
SqlDataAdapter sda2 = new SqlDataAdapter(cmd2);
sda2.Fill(dt2);
con.Close();
ForecastTable dt102 = TimeSeries.naive(GetInputforheartbeat(), 1, 6);
GridView4.DataSource = dt102;
GridView4.DataBind();
ForecastTable dt112 = TimeSeries.weightedMovingAverage(GetInputforheartbeat(), 1, (Decimal)0.05, (Decimal)0.15, (Decimal)0.8);
GridView5.DataSource = dt112;
GridView5.DataBind();
foreach (GridViewRow row in GridView4.Rows)
{
string val = GridView3.Rows[5].Cells[2].Text;
// Label2.Text = "Upcoming Of State:" + ddlarea.Text + "of crime:" + DropDownList1.Text + val;
}
}
