#ifndef TRANSFORMATION_H
#define TRANSFORMATION_H
#include <vector>

using namespace std;

class Transformation
{
private:
	vector <double> SeasonalIndex;
	double GeometricAverage;
	double valueMean;
	double valueSd;
	int	tsSizeValue;

public:
	vector <double> TimeSeries;
	double INFINITY;
	Transformation(vector<double>);
	~Transformation();

	// период сезонности для использования в Deseasonalization 
	int seasonalPeriod;

	// параметр трансформации 
	double lambda;
	// параметр сдвига значений вектора при наличии в нем отрицательных значений
	double tsShiftValue;

	// обрабатываемый временной ряд
	void setTimeSeries(vector<double>);

	int sign(double);
	// auto lambda estimation
	void lambdaEstimation();

	//Guerrero(1993)
	//Time-series analysis supported by Power Transformations
	double lambdaEstimationGuerrero(double);

	//Box-Cox(1964)
	vector<double> BoxCox();
	vector<double> invBoxCox();

	//Manly(1976)
	// -- Negative y’s could be allowed
	// -- The transformation was reported to be successful in transform
	// unimodal skewed distribution into normal distribution, but is
	// not quite useful for bimodal or U - shaped distribution
	vector<double> ExponentialTransformation();
	vector<double> invExponentialTransformation();

	//John and Draper(1980)
	// -- Negative y’s could be allowed.
	// -- It works best at those distribution that is somewhat symmetric.
	//	A power transformation on a symmetric distribution is likel
	//	y going to introduce some degree of skewness.
	vector<double> ModulusTransformation();
	vector<double> invModulusTransformation();

	//Bickel and Doksum(1981)
	// --Negative y’s could be allowed.
	vector<double> AsymptoticBoxCox();
	vector<double> invAsymptoticBoxCox();

	//Yeo and Johnson (2000)
	// --Negative y’s could be allowed.
	// --When estimating the transformation parameter, found the value of
	//	λ that minimizes the Kullback - Leibler distance between
	//	the normal distribution and the transformed distribution.
	vector<double> ModifiedBoxCox();
	vector<double> invModifiedBoxCox();

	// Atkinson(1973)
	// --Negative y’s could be allowed.
	vector<double> NormalizedBoxCox();
	vector<double> invNormalizedBoxCox();

	// очистка вектора TimeSeries от сезонности
	vector<double> Deseasonalization(int);
	vector<double> invDeseasonalization();

	// получение средних сезоональных индексов
	vector<double> getSeasonalIndex();

	// Aggregation time series
	vector <double> Aggregation(int);
	vector <double> invAggregation();

	// Normalization time series: mean = 0, Sd (Standard deviation) = 1 
	vector <double> Normalization();
	vector <double> invNormalization();

};

#endif