#include <autocorr.h>
Public Methods | |
autocorr (double lim=0.01, size_t numPts=32) | |
~autocorr () | |
void | ACF (const double *v, const size_t N, acorrInfo &info) |
Calculate the autocorrelation function. More... | |
Private Methods | |
autocorr (const autocorr &rhs) | |
void | acorrSlope (acorrInfo &info) |
Calculate the slope the the log of the autocorrelation curve. More... | |
Private Attributes | |
const double | limit_ |
const size_t | numPoints_ |
You may use this source code without limitation and without fee as long as you include:
This software was written and is copyrighted by Ian Kaplan, Bear Products International, www.bearcave.com, 2001.
This software is provided "as is", without any warranty or claim as to its usefulness. Anyone who uses this source code uses it at their own risk. Nor is any support provided by Ian Kaplan and Bear Products International.
Please send any bug fixes or suggested source changes to:
iank@bearcave.com
Definition at line 37 of file autocorr.h.
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Definition at line 76 of file autocorr.h. 00076 : limit_(lim), 00077 numPoints_(numPts) {} |
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Definition at line 78 of file autocorr.h. 00078 {} |
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Calculate the autocorrelation function.
Definition at line 50 of file autocorr.cpp. Referenced by main().
00053 { 00054 if (! info.points().empty() ) { 00055 info.points().clear(); 00056 } 00057 00058 // The devMean array will contain the deviation from the mean. 00059 // That is, v[i] - mean(v). 00060 double *devMean = new double[ N ]; 00061 00062 double sum; 00063 size_t i; 00064 00065 // Calculate the mean and copy the vector v, into devMean 00066 sum = 0; 00067 for (i = 0; i < N; i++) { 00068 sum = sum + v[i]; 00069 devMean[i] = v[i]; 00070 } 00071 double mean = sum / static_cast<double>(N); 00072 00073 // Compute the values for the devMean array. Also calculate the 00074 // denominator for the autocorrelation function. 00075 sum = 0; 00076 for (i = 0; i < N; i++) { 00077 devMean[i] = devMean[i] - mean; 00078 sum = sum + (devMean[i]*devMean[i]); 00079 } 00080 double denom = sum / static_cast<double>(N); 00081 00082 // Calculate a std::vector of values which will make up 00083 // the autocorrelation function. 00084 double cor = 1.0; 00085 for (size_t shift = 1; shift <= numPoints_ && cor > limit_; shift++) { 00086 info.points().push_back( cor ); 00087 size_t n = N - shift; 00088 sum = 0.0; 00089 for (i = 0; i < n; i++) { 00090 sum = sum + (devMean[i] * devMean[i+shift]); 00091 } 00092 double numerator = sum / static_cast<double>(n) 00093 cor = numerator / denom; 00094 } 00095 00096 // calculate the log/log slope of the autocorrelation 00097 acorrSlope( info ); 00098 00099 delete [] m; 00100 } // ACF |
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Calculate the slope the the log of the autocorrelation curve. Autocorrelation is one measure for long memory (long range dependence) in a data set. The idea behind implementing this function was that the slope of the log/log plot of the autocorrelation curve could be compared for various data sets. The Hurst exponent provides another measure for long memory processes. My original idea was to use the slope of the log/log ACF as a comparision for the various Hurst values. As it turned out, the slope of the ACF was not a very useful metric since the slopes did not differ much for various Hurst exponents Definition at line 27 of file autocorr.cpp. Referenced by ACF().
00028 { 00029 00030 const size_t len = info.points().size(); 00031 if (len > 0) { 00032 lregress::lineInfo regressInfo; 00033 lregress lr; 00034 vector<lregress::point> regressPoints; 00035 for (size_t i = 0; i < len; i++) { 00036 double x = log(i+1); 00037 double y = log((info.points())[i]); 00038 regressPoints.push_back( lregress::point(x,y) ); 00039 } 00040 lr.lr( regressPoints, regressInfo ); 00041 info.slope( regressInfo.slope() ); 00042 info.slopeErr( regressInfo.slopeErr() ); 00043 } 00044 } // acorrSlope |
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Definition at line 42 of file autocorr.h. |
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Definition at line 44 of file autocorr.h. |