563 lines
20 KiB
MQL5
563 lines
20 KiB
MQL5
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//-----------------------------------------------------------------------------------
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// TSAnalysisMod.mqh
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// Copyright (c) 2012-2020, victorg, Marketeer
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// https://www.mql5.com/en/users/marketeer
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//-----------------------------------------------------------------------------------
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#property copyright "Copyright (c) 2012-2020, victorg, Marketeer"
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#property link "https://www.mql5.com/en/users/marketeer"
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#property version "3.0"
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struct TSStatMeasures
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{
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double MinTS; // Minimum time series value
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double MaxTS; // Maximum time series value
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double Median; // Median
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double Mean; // Mean (average)
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double Var; // Variance
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double uVar; // Unbiased variance
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double StDev; // Standard deviation
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double uStDev; // Unbiaced standard deviation
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double Skew; // Skewness
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double Kurt; // Kurtosis
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double ExKurt; // Excess Kurtosis
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double JBTest; // Jarque-Bera test
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double JBpVal; // JB test p-value
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double AJBTest; // Adjusted Jarque-Bera test
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double AJBpVal; // AJB test p-values
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double maxOut; // Sequence Plot. Border of outliers
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double minOut; // Sequence Plot. Border of outliers
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double UPLim; // ACF. Upper limit (5% significance level)
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double LOLim; // ACF. Lower limit (5% significance level)
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int NLags; // Number of lags for ACF and PACF Plot
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int IP; // Autoregressive model order
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};
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#define TS_GEN_ARRAY(NAME,ARRAY) \
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int get##NAME(double &result[]) const \
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{ \
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ArrayResize(result, ArraySize(ARRAY)); \
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return ArrayCopy(result, ARRAY); \
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}
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#define TS_STATS(NAME) result.NAME = NAME;
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#define TS_GEN_SWITCH(ELEMENT) case tsa_##ELEMENT: return get##ELEMENT(result);
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enum TSA_TYPE
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{
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tsa_TimeSeries,
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tsa_TimeSeriesSorted,
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tsa_TimeSeriesCentered,
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tsa_HistogramX,
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tsa_HistogramY,
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tsa_NormalProbabilityX,
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tsa_ACF,
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tsa_ACFConfidenceBandUpper,
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tsa_ACFConfidenceBandLower,
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tsa_ACFSpectrumY,
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tsa_PACF,
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tsa_ARSpectrumY,
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tsa_Size //
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}; // ^ non-breaking space (to hide aux element tsa_Size name)
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//-----------------------------------------------------------------------------------
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class TSAnalysis
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{
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protected:
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double TS[]; // Time series
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double TSort[]; // Sorted time series
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double TSCenter[]; // Centered time series ( TS[] - mean )
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int NumTS; // Number of time series data points
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double MinTS; // Minimum time series value
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double MaxTS; // Maximum time series value
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double Median; // Median
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double Mean; // Mean (average)
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double Var; // Variance
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double uVar; // Unbiased variance
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double StDev; // Standard deviation
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double uStDev; // Unbiaced standard deviation
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double Skew; // Skewness
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double Kurt; // Kurtosis
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double ExKurt; // Excess Kurtosis
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double JBTest; // Jarque-Bera test
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double JBpVal; // JB test p-value
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double AJBTest; // Adjusted Jarque-Bera test
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double AJBpVal; // AJB test p-values
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double maxOut; // Sequence Plot. Border of outliers
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double minOut; // Sequence Plot. Border of outliers
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double XHist[]; // Histogram. X-axis
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double YHist[]; // Histogram. Y-axis
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double Xnpp[]; // Normal Probability Plot. X-axis
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int NLags; // Number of lags for ACF and PACF Plot
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double ACF[]; // Autocorrelation function (correlogram)
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double UPLim; // ACF. Upper limit (5% significance level)
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double LOLim; // ACF. Lower limit (5% significance level)
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double CBup[]; // ACF. Upper limit (confidence bands)
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double CBlo[]; // ACF. Lower limit (confidence bands)
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double Spect[]; // ACF Spectrum. Y-axis
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double PACF[]; // Partial autocorrelation function
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int IP; // Autoregressive model order
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double ARSp[]; // AR Spectrum. Y-axis
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public:
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TSAnalysis();
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TSAnalysis(const double &ts[]);
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void Calc(const double &ts[]); // Main calculation
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void getStatMeasures(TSStatMeasures &result)
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{
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TS_STATS(MinTS);
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TS_STATS(MaxTS);
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TS_STATS(Median);
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TS_STATS(Mean);
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TS_STATS(Var);
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TS_STATS(uVar);
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TS_STATS(StDev);
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TS_STATS(uStDev);
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TS_STATS(Skew);
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TS_STATS(Kurt);
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TS_STATS(ExKurt);
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TS_STATS(JBTest);
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TS_STATS(JBpVal);
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TS_STATS(AJBTest);
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TS_STATS(AJBpVal);
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TS_STATS(maxOut);
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TS_STATS(minOut);
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TS_STATS(UPLim);
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TS_STATS(LOLim);
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TS_STATS(NLags);
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TS_STATS(IP);
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}
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TS_GEN_ARRAY(TimeSeries, TS)
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TS_GEN_ARRAY(TimeSeriesSorted, TSort)
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TS_GEN_ARRAY(TimeSeriesCentered, TSCenter)
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TS_GEN_ARRAY(HistogramX, XHist);
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TS_GEN_ARRAY(HistogramY, YHist);
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TS_GEN_ARRAY(NormalProbabilityX, Xnpp);
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TS_GEN_ARRAY(ACF, ACF);
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TS_GEN_ARRAY(ACFConfidenceBandUpper, CBup);
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TS_GEN_ARRAY(ACFConfidenceBandLower, CBlo);
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TS_GEN_ARRAY(ACFSpectrumY, Spect);
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TS_GEN_ARRAY(PACF, PACF);
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TS_GEN_ARRAY(ARSpectrumY, ARSp);
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int getResult(const TSA_TYPE type, double &result[]) const
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{
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switch(type)
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{
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TS_GEN_SWITCH(TimeSeries);
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TS_GEN_SWITCH(TimeSeriesSorted)
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TS_GEN_SWITCH(TimeSeriesCentered)
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TS_GEN_SWITCH(HistogramX);
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TS_GEN_SWITCH(HistogramY);
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TS_GEN_SWITCH(NormalProbabilityX);
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TS_GEN_SWITCH(ACF);
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TS_GEN_SWITCH(ACFConfidenceBandUpper);
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TS_GEN_SWITCH(ACFConfidenceBandLower);
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TS_GEN_SWITCH(ACFSpectrumY);
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TS_GEN_SWITCH(PACF);
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TS_GEN_SWITCH(ARSpectrumY);
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//case tsa_#ELEMENT: return get##ELEMENT(result);
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}
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return 0;
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}
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protected:
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double ndtri(double y); // Inverse of Normal distribution function
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void LevinsonRecursion(const double &R[], double &A[], double &K[]);
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void fht(double &f[], ulong ldn); // Fast Hartley Transform
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};
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//-----------------------------------------------------------------------------------
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// Constructor
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//-----------------------------------------------------------------------------------
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void TSAnalysis::TSAnalysis()
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{
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}
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void TSAnalysis::TSAnalysis(const double &ts[])
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{
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Calc(ts);
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}
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//-----------------------------------------------------------------------------------
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// Main calculation
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//-----------------------------------------------------------------------------------
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void TSAnalysis::Calc(const double &ts[])
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{
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int i, k, m, n, p;
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double sum2, sum3, sum4, a, b, c, v, delta;
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double cor[], ar[], tdat[];
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NumTS = ArraySize(ts); // Number of time series data points
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if(NumTS < 8) // Number of data points is too small
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{
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Print("TSAnalysis: Error. Number of TS data points is too small!");
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return;
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}
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ArrayResize(TS, NumTS);
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ArrayCopy(TS, ts); // Time series
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ArrayResize(TSort, NumTS);
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ArrayCopy(TSort, ts);
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ArraySort(TSort); // Sorted time series
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MinTS = TSort[0]; // Minimum time series value
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MaxTS = TSort[NumTS - 1]; // Maximum time series value
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i = (NumTS - 1) / 2;
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Median = TSort[i]; // Median
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if((NumTS & 0x01) == 0) Median = (Median + TSort[i + 1]) / 2.0; // Median
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Mean = 0;
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sum2 = 0;
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sum3 = 0;
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sum4 = 0;
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for(i = 0; i < NumTS; i++)
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{
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n = i + 1;
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delta = TS[i] - Mean;
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a = delta / n;
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Mean += a; // Mean (average)
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sum4 += a * (a * a * delta * i * (n * (n - 3.0) + 3.0) + 6.0 * a * sum2 - 4.0 * sum3); // sum of fourth degree
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b = TS[i] - Mean;
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sum3 += a * (b * delta * (n - 2.0) - 3.0 * sum2); // sum of third degree
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sum2 += delta * b; // sum of second degree
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}
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if(sum2 < 1.e-250) // variance is too small
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{
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Print("TSAnalysis: Error. The variance is too small or zero!");
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return;
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}
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ArrayResize(TSCenter, NumTS);
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for(i = 0; i < NumTS; i++)
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TSCenter[i] = TS[i] - Mean; // Centered time series
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Var = sum2 / NumTS; // Variance
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uVar = sum2 / (NumTS - 1); // Unbiased variance
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StDev = MathSqrt(Var); // Standard deviation
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uStDev = MathSqrt(uVar); // Unbiased standard deviation
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Skew = MathSqrt(NumTS) * sum3 / sum2 / MathSqrt(sum2); // Skewness
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Kurt = NumTS * sum4 / sum2 / sum2; // Kurtosis
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ExKurt = Kurt - 3; // Excess kurtosis
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JBTest = (NumTS / 6.0) * (Skew * Skew + ExKurt * ExKurt / 4); // Jarque-Bera test
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JBpVal = MathExp(-JBTest / 2.0); // JB test p-value
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a = 6 * (NumTS - 2.0) / (NumTS + 1.0) / (NumTS + 3.0);
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b = 3 * (NumTS - 1.0) / (NumTS + 1.0);
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AJBTest = Skew * Skew / a + (Kurt - b) * (Kurt - b) / // Adjusted Jarque-Bera test
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(24.0 * NumTS * (NumTS - 2.0) * (NumTS - 3.0) / (NumTS + 1.0) /
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(NumTS + 1.0) / (NumTS + 3.0) / (NumTS + 5.0));
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AJBpVal = MathExp(-AJBTest / 2.0); // AJB test p-value
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// Time Series Plot. Y=TS[],line1=maxOut,line2=Mean,line3=minOut
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delta = (1.55 + 0.8 * MathLog10(NumTS / 10.0) * MathSqrt(Kurt - 1)) * StDev;
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maxOut = Mean + delta; // Time Series Plot. Border of outliers
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minOut = Mean - delta; // Time Series Plot. Border of outliers
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// Histogram. X=XHist[],Y=YHist[]
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n = (int)MathRound((Kurt + 1.5) * MathPow(NumTS, 0.4) / 6.0);
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if((n & 0x01) == 0) n--;
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if(n < 5) n = 5; // Number of bins
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ArrayResize(XHist, n);
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ArrayResize(YHist, n);
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ArrayInitialize(YHist, 0.0);
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a = MathAbs(TSort[0] - Mean);
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b = MathAbs(TSort[NumTS - 1] - Mean);
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if(a < b) a = b;
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v = Mean - a;
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delta = 2.0 * a / n;
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for(i = 0; i < n; i++)
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XHist[i] = (v + (i + 0.5) * delta - Mean) / StDev; // Histogram. X-axis
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for(i = 0; i < NumTS; i++)
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{
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k = (int)((TS[i] - v) / delta);
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if(k > (n - 1)) k = n - 1;
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YHist[k]++;
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}
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for(i = 0; i < n; i++)
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YHist[i] = YHist[i] / NumTS / delta * StDev; // Histogram. Y-axis
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// Normal Probability Plot. X=Xnpp[],Y=TSort[]
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ArrayResize(Xnpp, NumTS);
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Xnpp[NumTS - 1] = MathPow(0.5, 1.0 / NumTS);
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Xnpp[0] = 1 - Xnpp[NumTS - 1];
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a = NumTS + 0.365;
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for(i = 1; i < (NumTS - 1); i++)
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Xnpp[i] = (i + 0.6825) / a;
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for(i = 0; i < NumTS; i++)
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Xnpp[i] = ndtri(Xnpp[i]); // Normal Probability Plot. X-axis
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// Autocorrelation function (correlogram)
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NLags = (int)MathRound(50 * MathLog(NumTS));
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if(NLags > NumTS / 2) NLags = NumTS / 2;
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if(NLags < 3) NLags = 3; // Number of lags for ACF and PACF Plot
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IP = NLags * 5;
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if(IP > NumTS * 0.7) IP = (int)MathRound(NumTS * 0.7); // Autoregressive model order
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ArrayResize(cor, IP);
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ArrayResize(ar, IP);
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ArrayResize(tdat, IP);
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a = 0;
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for(i = 0; i < NumTS; i++)
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a += TSCenter[i] * TSCenter[i];
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for(i = 1; i <= IP; i++)
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{
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c = 0;
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for(k = i; k < NumTS; k++)
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c += TSCenter[k] * TSCenter[k - i];
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cor[i - 1] = c / a; // Autocorrelation
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}
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LevinsonRecursion(cor, ar, tdat); // Levinson-Durbin recursion
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ArrayResize(ACF, NLags);
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ArrayCopy(ACF, cor, 0, 0, NLags); // ACF
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ArrayResize(PACF, NLags);
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ArrayCopy(PACF, tdat, 0, 0, NLags); // PACF
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UPLim = 1.96 / MathSqrt(NumTS); // Upper limit (5% significance level)
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LOLim = -UPLim; // Lower limit (5% significance level)
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ArrayResize(CBup, NLags);
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ArrayResize(CBlo, NLags);
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a = 0;
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for(i = 0; i < NLags; i++)
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{
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a += ACF[i] * ACF[i];
|
|||
|
|
CBup[i] = 1.96 * MathSqrt((1 + 2 * a) / NumTS); // Upper limit (confidence bands)
|
|||
|
|
CBlo[i] = -CBup[i]; // Lower limit (confidence bands)
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Spectrum Plot
|
|||
|
|
n = 320; // Number of X-points
|
|||
|
|
ArrayResize(Spect, n);
|
|||
|
|
v = M_PI / n;
|
|||
|
|
for(i = 0; i < n; i++)
|
|||
|
|
{
|
|||
|
|
a = i * v;
|
|||
|
|
b = 0;
|
|||
|
|
for(k = 0; k < NLags; k++)
|
|||
|
|
b += ((double)NLags - k) / (NLags + 1.0) * ACF[k] * MathCos(a * (k + 1));
|
|||
|
|
Spect[i] = 2.0 * (1 + 2 * b); // Spectrum Y-axis
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// AR Spectral Estimates Plot (maximum entropy method)
|
|||
|
|
p = 12; // n = 2**p = 4096
|
|||
|
|
n = ((ulong)1 << p); // Number of X-points
|
|||
|
|
m = n << 1;
|
|||
|
|
ArrayResize(ARSp, n); // AR Spectrum. Y-axis
|
|||
|
|
ArrayResize(tdat, m);
|
|||
|
|
ArrayInitialize(tdat, 0);
|
|||
|
|
tdat[0] = 1;
|
|||
|
|
for(i = 0; i < IP; i++)
|
|||
|
|
tdat[i + 1] = -ar[i];
|
|||
|
|
fht(tdat, p + 1); // Fast Hartley transform (FHT)
|
|||
|
|
for(k = 1, i = m - 1; k < i; ++k, --i)
|
|||
|
|
tdat[k] = tdat[k] * tdat[k] + tdat[i] * tdat[i];
|
|||
|
|
tdat[0] = 2 * tdat[0] * tdat[0];
|
|||
|
|
ArrayCopy(ARSp, tdat, 0, 0, n);
|
|||
|
|
c = -DBL_MAX;
|
|||
|
|
for(i = 0; i < n; i++)
|
|||
|
|
{
|
|||
|
|
ARSp[i] = 1 / ARSp[i];
|
|||
|
|
if(c < ARSp[i]) c = ARSp[i]; // c = max(ARSp)
|
|||
|
|
}
|
|||
|
|
for(i = 0; i < n; i++) // logarithmic scale
|
|||
|
|
{
|
|||
|
|
b = ARSp[i] / c; // normalization
|
|||
|
|
if(b < 1e-7) b = 1e-7;
|
|||
|
|
ARSp[i] = 10 * MathLog10(b); // dB
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//-----------------------------------------------------------------------------------
|
|||
|
|
// Inverse of Normal distribution function
|
|||
|
|
// Prototype:
|
|||
|
|
// Cephes Math Library Release 2.8: June, 2000
|
|||
|
|
// Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier
|
|||
|
|
//-----------------------------------------------------------------------------------
|
|||
|
|
double TSAnalysis::ndtri(double y0)
|
|||
|
|
{
|
|||
|
|
static double s2pi = 2.50662827463100050242E0; // sqrt(2pi)
|
|||
|
|
static double P0[5] = {-5.99633501014107895267E1, 9.80010754185999661536E1,
|
|||
|
|
-5.66762857469070293439E1, 1.39312609387279679503E1,
|
|||
|
|
-1.23916583867381258016E0};
|
|||
|
|
static double Q0[8] = {1.95448858338141759834E0, 4.67627912898881538453E0,
|
|||
|
|
8.63602421390890590575E1, -2.25462687854119370527E2,
|
|||
|
|
2.00260212380060660359E2, -8.20372256168333339912E1,
|
|||
|
|
1.59056225126211695515E1, -1.18331621121330003142E0};
|
|||
|
|
static double P1[9] = {4.05544892305962419923E0, 3.15251094599893866154E1,
|
|||
|
|
5.71628192246421288162E1, 4.40805073893200834700E1,
|
|||
|
|
1.46849561928858024014E1, 2.18663306850790267539E0,
|
|||
|
|
-1.40256079171354495875E-1, -3.50424626827848203418E-2,
|
|||
|
|
-8.57456785154685413611E-4};
|
|||
|
|
static double Q1[8] = {1.57799883256466749731E1, 4.53907635128879210584E1,
|
|||
|
|
4.13172038254672030440E1, 1.50425385692907503408E1,
|
|||
|
|
2.50464946208309415979E0, -1.42182922854787788574E-1,
|
|||
|
|
-3.80806407691578277194E-2, -9.33259480895457427372E-4};
|
|||
|
|
static double P2[9] = {3.23774891776946035970E0, 6.91522889068984211695E0,
|
|||
|
|
3.93881025292474443415E0, 1.33303460815807542389E0,
|
|||
|
|
2.01485389549179081538E-1, 1.23716634817820021358E-2,
|
|||
|
|
3.01581553508235416007E-4, 2.65806974686737550832E-6,
|
|||
|
|
6.23974539184983293730E-9};
|
|||
|
|
static double Q2[8] = {6.02427039364742014255E0, 3.67983563856160859403E0,
|
|||
|
|
1.37702099489081330271E0, 2.16236993594496635890E-1,
|
|||
|
|
1.34204006088543189037E-2, 3.28014464682127739104E-4,
|
|||
|
|
2.89247864745380683936E-6, 6.79019408009981274425E-9};
|
|||
|
|
double x, y, z, y2, x0, x1, a, b;
|
|||
|
|
int i, code;
|
|||
|
|
|
|||
|
|
if(y0 <= 0.0)
|
|||
|
|
{
|
|||
|
|
Print("Function ndtri() error!");
|
|||
|
|
return (-DBL_MAX);
|
|||
|
|
}
|
|||
|
|
if(y0 >= 1.0)
|
|||
|
|
{
|
|||
|
|
Print("Function ndtri() error!");
|
|||
|
|
return (DBL_MAX);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
code = 1;
|
|||
|
|
y = y0;
|
|||
|
|
if(y > (1.0 - 0.13533528323661269189))
|
|||
|
|
{
|
|||
|
|
y = 1.0 - y;
|
|||
|
|
code = 0;
|
|||
|
|
} // 0.135... = exp(-2)
|
|||
|
|
if(y > 0.13533528323661269189) // 0.135... = exp(-2)
|
|||
|
|
{
|
|||
|
|
y = y - 0.5;
|
|||
|
|
y2 = y * y;
|
|||
|
|
a = P0[0];
|
|||
|
|
for(i = 1; i < 5; i++)
|
|||
|
|
a = a * y2 + P0[i];
|
|||
|
|
b = y2 + Q0[0];
|
|||
|
|
for(i = 1; i < 8; i++)
|
|||
|
|
b = b * y2 + Q0[i];
|
|||
|
|
x = y + y * (y2 * a / b);
|
|||
|
|
x = x * s2pi;
|
|||
|
|
return (x);
|
|||
|
|
}
|
|||
|
|
x = MathSqrt(-2.0 * MathLog(y));
|
|||
|
|
x0 = x - MathLog(x) / x;
|
|||
|
|
z = 1.0 / x;
|
|||
|
|
if(x < 8.0) // y > exp(-32) = 1.2664165549e-14
|
|||
|
|
{
|
|||
|
|
a = P1[0];
|
|||
|
|
for(i = 1; i < 9; i++)
|
|||
|
|
a = a * z + P1[i];
|
|||
|
|
b = z + Q1[0];
|
|||
|
|
for(i = 1; i < 8; i++)
|
|||
|
|
b = b * z + Q1[i];
|
|||
|
|
x1 = z * a / b;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
a = P2[0];
|
|||
|
|
for(i = 1; i < 9; i++)
|
|||
|
|
a = a * z + P2[i];
|
|||
|
|
b = z + Q2[0];
|
|||
|
|
for(i = 1; i < 8; i++)
|
|||
|
|
b = b * z + Q2[i];
|
|||
|
|
x1 = z * a / b;
|
|||
|
|
}
|
|||
|
|
x = x0 - x1;
|
|||
|
|
if(code != 0) x = -x;
|
|||
|
|
|
|||
|
|
return (x);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//-----------------------------------------------------------------------------------
|
|||
|
|
// Calculate the Levinson-Durbin recursion for the autocorrelation sequence R[]
|
|||
|
|
// and return the autoregression coefficients A[] and partial autocorrelation
|
|||
|
|
// coefficients K[]
|
|||
|
|
//-----------------------------------------------------------------------------------
|
|||
|
|
void TSAnalysis::LevinsonRecursion(const double &R[], double &A[], double &K[])
|
|||
|
|
{
|
|||
|
|
int p, i, m;
|
|||
|
|
double km, Em, Am1[], err;
|
|||
|
|
|
|||
|
|
p = ArraySize(R);
|
|||
|
|
ArrayResize(Am1, p);
|
|||
|
|
ArrayInitialize(Am1, 0);
|
|||
|
|
ArrayInitialize(A, 0);
|
|||
|
|
ArrayInitialize(K, 0);
|
|||
|
|
km = 0;
|
|||
|
|
Em = 1;
|
|||
|
|
for(m = 0; m < p; m++)
|
|||
|
|
{
|
|||
|
|
err = 0;
|
|||
|
|
for(i = 0; i < m; i++)
|
|||
|
|
err += Am1[i] * R[m - i - 1];
|
|||
|
|
km = (R[m] - err) / Em;
|
|||
|
|
K[m] = km;
|
|||
|
|
A[m] = km;
|
|||
|
|
for(i = 0; i < m; i++)
|
|||
|
|
A[i] = (Am1[i] - km * Am1[m - i - 1]);
|
|||
|
|
Em = (1 - km * km) * Em;
|
|||
|
|
ArrayCopy(Am1, A);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//-----------------------------------------------------------------------------------
|
|||
|
|
// Radix-2 decimation in frequency (DIF) fast Hartley transform (FHT).
|
|||
|
|
// Length is N = 2 ** ldn
|
|||
|
|
//-----------------------------------------------------------------------------------
|
|||
|
|
void TSAnalysis::fht(double &f[], ulong ldn)
|
|||
|
|
{
|
|||
|
|
const ulong n = ((ulong)1 << ldn);
|
|||
|
|
for(ulong ldm = ldn; ldm >= 1; --ldm)
|
|||
|
|
{
|
|||
|
|
const ulong m = ((ulong)1 << ldm);
|
|||
|
|
const ulong mh = (m >> 1);
|
|||
|
|
const ulong m4 = (mh >> 1);
|
|||
|
|
const double phi0 = M_PI / (double)mh;
|
|||
|
|
for(ulong r = 0; r < n; r += m)
|
|||
|
|
{
|
|||
|
|
for(ulong j = 0; j < mh; ++j)
|
|||
|
|
{
|
|||
|
|
uint t1 = (uint)(r + j);
|
|||
|
|
uint t2 = (uint)(t1 + mh);
|
|||
|
|
double u = f[t1];
|
|||
|
|
double v = f[t2];
|
|||
|
|
f[t1] = u + v;
|
|||
|
|
f[t2] = u - v;
|
|||
|
|
}
|
|||
|
|
double ph = 0.0;
|
|||
|
|
for(ulong j = 1; j < m4; ++j)
|
|||
|
|
{
|
|||
|
|
ulong k = mh - j;
|
|||
|
|
ph += phi0;
|
|||
|
|
double s = MathSin(ph);
|
|||
|
|
double c = MathCos(ph);
|
|||
|
|
uint t1 = (uint)(r + mh + j);
|
|||
|
|
uint t2 = (uint)(r + mh + k);
|
|||
|
|
double pj = f[t1];
|
|||
|
|
double pk = f[t2];
|
|||
|
|
f[t1] = pj * c + pk * s;
|
|||
|
|
f[t2] = pj * s - pk * c;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
if(n > 2)
|
|||
|
|
{
|
|||
|
|
ulong r = 0;
|
|||
|
|
for(ulong i = 1; i < n; i++)
|
|||
|
|
{
|
|||
|
|
ulong k = n;
|
|||
|
|
do
|
|||
|
|
{
|
|||
|
|
k = k >> 1;
|
|||
|
|
r = r ^ k;
|
|||
|
|
} while((r & k) == 0);
|
|||
|
|
if(r > i)
|
|||
|
|
{
|
|||
|
|
double tmp = f[(uint)i];
|
|||
|
|
f[(uint)i] = f[(uint)r];
|
|||
|
|
f[(uint)r] = tmp;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
//-----------------------------------------------------------------------------------
|