private void decompose(int n, int freq, double[] season_)
{
int swindow = 10 * n + 1;
stllib.STL stl = new STL();
double[] seaonal = new double[freq];
int[] count = new int[freq];
int limit = (int)Math.Ceiling((double)n / freq);
this.freq = freq;
this.len = n;
unsafe
{
double *y = (double *)utils.Memory.Alloc(sizeof(double) * n);
double *t = (double *)utils.Memory.Alloc(sizeof(double) * n);
double *s = (double *)utils.Memory.Alloc(sizeof(double) * n);
for (int i = 0; i < n; i++)
{
y[i] = ts.data[i];
}
stl.compute(y, n, freq, swindow, t, s);
int k = 0;
if (type == ModelType.Explicit || type == ModelType.Implicit)
{
values = new double[limit];
for (int i = 0; i < limit; i++)
{
double t1 = 0;
double t2 = 0;
int l = 0;
for (int j = 0; j < freq; j++)
{
if (k == n)
{
break;
}
l++;
k++;
t1 += ts.data[j + i * freq];
t2 += y[j + i * freq];
}
t1 /= l;
t2 /= l;
values[i] = (t2 + t1) / 2;
}
}
if (type == ModelType.Implicit)
{
double[] x = ChebyshevReg.Solve(values);
values = new double[2];
values[0] = x[0];
values[1] = x[1];
}
else if (type == ModelType.Trend)
{
double[] t_ = new double[n];
for (int i = 0; i < n; i++)
{
t_[i] = t[i];
}
double[] x = ChebyshevReg.Solve(t_);
values = new double[2];
values[0] = x[0];
values[1] = x[1];
}
//make seaonality perfect
for (int i = 0; i < n; i++)
{
seaonal[i % freq] += s[i];
count[i % freq]++;
}
for (int i = 0; i < freq; i++)
{
season_[i] = seaonal[i % freq] / count[i % freq];
}
trend = new double[n];
for (int i = 0; i < n; i++)
{
trend[i] = t[i];
}
utils.Memory.Free(y);
utils.Memory.Free(s);
utils.Memory.Free(t);
}
}
public void Solve()
{
if (type == ModelType.PLA)
{
values = new double[2];
double asum = 0;
double bsum = 0;
for (int i = 0; i < this.ts.Length; i++)
{
asum += ((i + 1) - ((ts.Length + 1) / 2)) * ts.data[i];
bsum += ((i + 1) - ((2 * ts.Length + 1) / 3)) * ts.data[i];
}
values[0] = 12 * asum / ts.Length / (ts.Length + 1) / (ts.Length - 1);
values[1] = 6 * bsum / ts.Length / (1 - ts.Length);
return;
}
int n = ts.Length;
int l = 0;
freq = ts.freq[0];
while (freq > ts.Length)
{
if (l == ts.freq.Length)
{
return;
}
freq = ts.freq[l++];
}
if (freq == 0)
{
//use regression
values = ChebyshevReg.Solve(ts.data);
seasonal = null;
}
else
{
double[] season_ = new double[freq];
decompose(n, freq, season_);
seasonal = new Model();
int[] f;
if (ts.freq.Length == 1)
{
f = null;
}
else
{
f = new int[ts.freq.Length - 1];
for (int i = 0; i < ts.freq.Length - 1; i++)
{
f[i] = ts.freq[i + 1];
}
}
seasonal.ts = new TimeSeries(season_, f);
if (f != null)
{
seasonal.Solve();
}
}
}