public StlFitStats(Decomposition stl)
{
int length = stl.Data.Length;
// Unnecessary since STL guarantees this, so it can't be tested:
// Preconditions.checkArgument(length >= 4, "STL Decomposition must have at least 4 data points");
double[] data = stl.Data;
double[] trend = stl.Trend;
double[] seasonal = stl.Seasonal;
double[] residuals = stl.Residual;
double dataSum = 0;
double dataSqSum = 0;
double trendSum = 0;
double trendMax = -1E+100;
double trendMin = 1E+100;
double seasonalSum = 0;
double seasonalSqSum = 0;
double seasonalMax = -1E+100;
double seasonalMin = 1E+100;
double residualSum = 0;
double residualSqSum = 0;
double deSeasonalSum = 0;
double deSeasonalSqSum = 0;
double deTrendSum = 0;
double deTrendSqSum = 0;
for (int i = 0; (i < length); i++)
{
double d = data[i];
double t = trend[i];
double s = seasonal[i];
double r = residuals[i];
double f = d - s;
double dt = d - t;
dataSum = dataSum + d;
dataSqSum = dataSqSum + d * d;
trendSum = trendSum + t;
if (t > trendMax)
{
trendMax = t;
}
if (t < trendMin)
{
trendMin = t;
}
seasonalSum = seasonalSum + s;
seasonalSqSum = seasonalSqSum + s * s;
if (s > seasonalMax)
{
seasonalMax = s;
}
if (s < seasonalMin)
{
seasonalMin = s;
}
residualSum = residualSum + r;
residualSqSum = residualSqSum + r * r;
deSeasonalSum = deSeasonalSum + f;
deSeasonalSqSum = deSeasonalSqSum + f * f;
deTrendSum = deTrendSum + dt;
deTrendSqSum = deTrendSqSum + dt * dt;
}
double denom = 1.0 / length;
fDataMean = dataSum * denom;
fTrendMean = trendSum * denom;
fSeasonalMean = seasonalSum * denom;
fResidualMean = residualSum * denom;
fDeSeasonalMean = deSeasonalSum * denom;
fDeTrendMean = deTrendSum * denom;
// The data is from a valid STL decomposition, so length = 4 at minimum.
double corrBC = length / (length - 1.0); // Bessel's correction
double denomBC = 1.0 / (length - 1.0);
fDataVariance = dataSqSum * denomBC - fDataMean * fDataMean * corrBC;
fTrendRange = trendMax - trendMin;
fSeasonalVariance = seasonalSqSum * denomBC - fSeasonalMean * fSeasonalMean * corrBC;
fSeasonalRange = seasonalMax - seasonalMin;
fResidualVariance = residualSqSum * denomBC - fResidualMean * fResidualMean * corrBC;
fDeSeasonalVariance = deSeasonalSqSum * denomBC - fDeSeasonalMean * fDeSeasonalMean * corrBC;
fDeTrendVariance = deTrendSqSum * denomBC - fDeTrendMean * fDeTrendMean * corrBC;
fResidualVarMLE = denom * residualSqSum;
fResidualLogLikelihood = -0.5 * length * (1 + Math.Log(2 * Math.PI * fResidualVarMLE));
fSampleSize = length;
}