public TimeSeries(TimeSeries ts, Range r)
{
this.data = new double[(int)r.len];
this.r = r;
for (int i = (int)r.s; i <= (int)r.e; i++)
data[i - (int)r.s] = ts.data[i];
this.freq = ts.freq;
}
public ArrayList Divide(ArrayList ranges)
{
ArrayList tss = new ArrayList();
foreach( Range r in ranges) {
ArrayList tmp=new ArrayList();
for(int i=(int)r.s;i<=(int)r.e;i++){
tmp.Add(data[i]);
}
TimeSeries ts=new TimeSeries(tmp, freq);
ts.r = r;
tss.Add(ts);
}
return tss;
}
public RTree(ArrayList ranges, TimeSeries ts)
{
ArrayList R_j = (ArrayList)ranges[ranges.Count - 1];
root = (Range)R_j[0];
ArrayList R_i;
for (int i = ranges.Count - 1; i >= 0; i--)
{
R_i = (ArrayList)ranges[i];
foreach (Range r_i in R_i)
{
TimeSeries ts_ = new TimeSeries(ts, r_i);
Model m = new Model(ts_);
m.Clean();
m.Set();
ModelTree t = m.construct();
t.range = r_i;
t.children = null;
r_i.t = t;
}
}
for (int i = ranges.Count - 2; i >= 0; i--)
{
R_i = (ArrayList)ranges[i];
R_j = (ArrayList)ranges[i + 1];
foreach (Range r_i in R_i)
{
foreach (Range r_j in R_j)
{
if (r_j.overlap(r_i))
{
r_j.children.Add(r_i);
}
}
}
}
for (int i = ranges.Count - 2; i >= 0; i--)
{
R_i = (ArrayList)ranges[i];
R_j = (ArrayList)ranges[i + 1];
foreach (Range r_i in R_i)
{
foreach (Range r_j in R_j)
{
if (r_j.overlap(r_i))
{
if (r_j.len > r_i.len)
{
if (r_i.t.error > 1)
{
r_j.t.childs.Add(r_i.t);
r_i.t.parent = r_j.t;
}
}
}
}
}
}
//convert childs to children
for (int i = ranges.Count - 1; i >= 0; i--)
{
R_i = (ArrayList)ranges[i];
foreach (Range r_i in R_i)
{
r_i.t.convertchildstochildren();
}
}
}
public void getModels(int len, double error, ref int length)
{
length = 0;
int kk = (int)Math.Floor((double)ts.Length / len);
models = new Model[kk];
int x = 0;
this.len = len;
for (int i = 0; i < kk; i++)
{
if (i == kk - 1) len = ts.Length - x;
int s = x;
double[] u = new double[len];
for (int j = 0; j < len; j++) u[j] = ts.data[x++];
TimeSeries t = new TimeSeries(u, ts.freq);
Model m = new Model(t);
length += m.countError(error);
m.start = s;
models[i] = m;
}
}
public ModelSet(TimeSeries ts)
{
this.ts = ts;
}
void setModels(int len, double[] errors, int shift, int done)
{
double[] newerrors = null;
if (errors.Length > 1)
{
newerrors = new double[errors.Length - 1];
for (int i = 0; i < errors.Length - 1; i++)
{
newerrors[i] = errors[i];
}
}
int kk = (int)Math.Floor((double)ts.Length / len);
children = new ModelTree[kk];
int x = 0;
for (int i = 0; i < kk; i++)
{
if (i == kk - 1) len = ts.Length - x;
int s = x;
double[] u = new double[len];
for (int j = 0; j < len; j++) u[j] = ts.data[x++];
TimeSeries t = new TimeSeries(u, ts.freq);
ModelTree m = null;
if (shift == 1)
m = new ModelTree(t, newerrors, s);
else
m = new ModelTree(t, errors, s);
if (done == 0)
{
m.BuildTree();
}
children[i] = m;
m.start = s;
}
}
public ModelTree(TimeSeries ts, double[] errors = null, int start = 0)
: base(ts)
{
this.range = new Range(start, start + ts.Length - 1);
this.errors = errors;
this.children = null;
}
public virtual void Clean()
{
errors = null;
trend = null;
if ((seasonal != null) && (values != null)) { ts = null; }
if (seasonal != null) seasonal.Clean();
}
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 Model()
{
ts = null;
seasonal = null;
values = null;
errors = null;
}
public Model(TimeSeries ts)
{
this.ts = ts;
seasonal = null;
values = null;
errors = null;
ModelType best = ModelType.Trend;
double min = double.MaxValue;
foreach (ModelType t in Enum.GetValues(typeof(ModelType)))
{
this.type = t;
this.Solve();
this.CalcError();
double rt = this.Size() * Error(Global.confidence);
if (min > rt) { min = rt; best = t; }
}
type = best;
this.Solve();
this.CalcError();
ComputeErrorRange();
error = Error(Global.confidence);
this.len = ts.Length;
}
public ModelSet(TimeSeries ts)
{
this.ts = ts;
}
public ArrayList Divide(int length)
{
ArrayList tss = new ArrayList();
ArrayList tmp= new ArrayList();
for (int i = 0; i < Length; i++)
{
tmp.Add(data[i]);
if(tmp.Count== length){
TimeSeries ts = new TimeSeries(tmp, freq);
ts.r = new Range(i-tmp.Count+1, i );
tss.Add(ts);
tmp.Clear();
}
}
if (tmp.Count > 0)
{
TimeSeries ts = new TimeSeries(tmp, freq);
ts.r = new Range(Length - tmp.Count - 1, Length);
tss.Add(ts);
}
return tss;
}
public static void GenUK()
{
//first read ukc
int[] freq = { 17520 };
double[] uk = utils.File.ReadData("c:/data/ukc.txt");
TimeSeries ts = new TimeSeries(uk, freq);
Model m = new Model(ts);
double [] errors = new double[m.trend.Length];
for (int i = 0; i < m.trend.Length; i++)
{
errors[i] = ts.data[i] - m.Eval(i);
}
double[] t = new double[m.trend.Length * 100];
double[] s = new double[m.trend.Length * 100];
double[] e = new double[m.trend.Length * 100];
Random r = new Random();
for (int i = 0; i < m.trend.Length; i++)
{
for (int j = 0; j < 100; j++)
{
t[j * m.trend.Length + i] = m.trend[i];
}
}
for (int i = 0; i < m.trend.Length * 100; i++)
{
double mult = 1;
if ((i < m.trend.Length * 10) && (i > 0))
{
mult = 1.1;
}
if ((i < m.trend.Length * 20) && (i > m.trend.Length * 10))
{
mult = 1.1;
}
if ((i < m.trend.Length * 30) && (i > m.trend.Length * 20))
{
mult = 1.0;
}
if ((i < m.trend.Length * 40) && (i > m.trend.Length * 30))
{
mult = 1.4;
}
if ((i < m.trend.Length * 50) && (i > m.trend.Length * 40))
{
mult = 1.2;
}
if ((i < m.trend.Length * 60) && (i > m.trend.Length * 50))
{
mult = 1.3;
}
if ((i < m.trend.Length * 70) && (i > m.trend.Length * 60))
{
mult = 1.5;
}
if ((i < m.trend.Length * 80) && (i > m.trend.Length * 70))
{
mult = 1.2;
}
if ((i < m.trend.Length * 90) && (i > m.trend.Length * 80))
{
mult = 1.1;
}
if ((i < m.trend.Length * 100) && (i > m.trend.Length * 90))
{
mult = 1.0;
}
s[i] = m.seasonal.Eval(i % freq[0]) * mult;
}
for (int i = 0; i < m.trend.Length * 100; i++)
{
e[i] = errors[i % m.trend.Length] * (r.NextDouble() / 2 + 0.5) * 0.001;
}
StreamWriter sw = new StreamWriter("c:/data/n/uk3.txt");
for (int i = 0; i < m.trend.Length * 30; i++)
{
double tt = t[i] + s[i] + e[i];
sw.WriteLine((int)tt);
}
sw.Close();
}
