static public Model ModelQuick(TimeSeries ts)
{
Model m = new Model();
m.ts = ts;
m.seasonal = null;
m.values = null;
m.errors = null;
m.type = ModelType.Explicit;
m.Solve();
m.CalcError();
// m.ComputeErrorRange();
m.error = m.Error(Global.confidence);
m.len = ts.Length;
return(m);
}
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();
}
}
public static Model ModelQuick(TimeSeries ts)
{
Model m = new Model();
m.ts = ts;
m.seasonal = null;
m.values = null;
m.errors = null;
m.type = ModelType.Explicit;
m.Solve();
m.CalcError();
// m.ComputeErrorRange();
m.error = m.Error(Global.confidence);
m.len = ts.Length;
return m;
}
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();
}
}
}