96 lines
2.8 KiB
C
96 lines
2.8 KiB
C
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#ifndef TRANSFORMATION_H
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#define TRANSFORMATION_H
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#include <vector>
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using namespace std;
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class Transformation
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{
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private:
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vector <double> SeasonalIndex;
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double GeometricAverage;
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double valueMean;
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double valueSd;
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int tsSizeValue;
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public:
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vector <double> TimeSeries;
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double INFINITY;
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Transformation(vector<double>);
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~Transformation();
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// период сезонности для использования в Deseasonalization
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int seasonalPeriod;
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// параметр трансформации
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double lambda;
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// параметр сдвига значений вектора при наличии в нем отрицательных значений
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double tsShiftValue;
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// обрабатываемый временной ряд
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void setTimeSeries(vector<double>);
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int sign(double);
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// auto lambda estimation
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void lambdaEstimation();
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//Guerrero(1993)
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//Time-series analysis supported by Power Transformations
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double lambdaEstimationGuerrero(double);
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//Box-Cox(1964)
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vector<double> BoxCox();
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vector<double> invBoxCox();
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//Manly(1976)
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// -- Negative y’s could be allowed
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// -- The transformation was reported to be successful in transform
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// unimodal skewed distribution into normal distribution, but is
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// not quite useful for bimodal or U - shaped distribution
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vector<double> ExponentialTransformation();
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vector<double> invExponentialTransformation();
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//John and Draper(1980)
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// -- Negative y’s could be allowed.
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// -- It works best at those distribution that is somewhat symmetric.
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// A power transformation on a symmetric distribution is likel
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// y going to introduce some degree of skewness.
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vector<double> ModulusTransformation();
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vector<double> invModulusTransformation();
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//Bickel and Doksum(1981)
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// --Negative y’s could be allowed.
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vector<double> AsymptoticBoxCox();
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vector<double> invAsymptoticBoxCox();
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//Yeo and Johnson (2000)
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// --Negative y’s could be allowed.
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// --When estimating the transformation parameter, found the value of
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// λ that minimizes the Kullback - Leibler distance between
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// the normal distribution and the transformed distribution.
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vector<double> ModifiedBoxCox();
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vector<double> invModifiedBoxCox();
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// Atkinson(1973)
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// --Negative y’s could be allowed.
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vector<double> NormalizedBoxCox();
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vector<double> invNormalizedBoxCox();
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// очистка вектора TimeSeries от сезонности
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vector<double> Deseasonalization(int);
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vector<double> invDeseasonalization();
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// получение средних сезоональных индексов
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vector<double> getSeasonalIndex();
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// Aggregation time series
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vector <double> Aggregation(int);
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vector <double> invAggregation();
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// Normalization time series: mean = 0, Sd (Standard deviation) = 1
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vector <double> Normalization();
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vector <double> invNormalization();
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};
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#endif
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