126 lines
3.5 KiB
C++
126 lines
3.5 KiB
C++
//
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// Ìåòîä ýêñïîíåíöèàëüíîãî ñãëàæèâàíèÿ è ïðîãíîçèðîâàíèÿ:
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// äåìïèíã àääèòèâíûé òðåíä, áåç ñåçîííîñòè
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//
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#include "StdAfx.h"
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#include <iostream>
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#include "DATrendNoSeasonality.h"
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#include "Param.h"
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// êîíñòðóêòîð ñ çàäàííûìè íåïîñðåäñòâåííî ïàðàìåòðàìè
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DATrendNoSeasonality::DATrendNoSeasonality(vector<double> timeSeries, int countPointForecast) {
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this->x = timeSeries;
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this->countPointForecast = countPointForecast;
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this->partition();
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}
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DATrendNoSeasonality::~DATrendNoSeasonality() {
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// îñâîáîæäàåòñÿ ïàìÿòü
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std::vector<double> ().swap(S);
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std::vector<double> ().swap(x);
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std::vector<double> ().swap(T);
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std::vector<double> ().swap(forecast);
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}
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// èíèöèàëèçàöèÿ ìîäåëè, çàäàíèå ïåðâîíà÷àëüíûõ çíà÷åíèé
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void DATrendNoSeasonality::init() {
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S.clear();
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T.clear();
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forecast.clear();
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T.push_back(x[1] - x[0]);
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S.push_back(x[0]);
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forecast.push_back(S[0] + T[0] * phi);
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}
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// Çàäàòü ïàðàìåòð
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void DATrendNoSeasonality::setParam(string paramName, double value) {
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if (paramName.compare("alpha") == 0) {
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this->alpha = value;
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} else if (paramName.compare("gamma") == 0) {
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this->gamma = value;
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} else if (paramName.compare("phi") == 0) {
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this->phi = value;
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}
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}
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// ñôîðìèðîâàòü ìîäåëü
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void DATrendNoSeasonality::createModel() {
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this->init(); // èíèöèàëèçèðîâàòü ìîäåëü
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double e = 0;
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//âûïîëíÿåòñÿ ïðîõîä ìîäåëè ïî ñãëàæèâàíèþ è ïðîãíîçèðîâàíèþ countPointForecast òî÷åê
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for (unsigned int t = 0; t < x.size()-1 + this->countPointForecast; t++) {
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// ïîêà íå äîøëè äî êîíöà ðÿäà - ñãëàæèâàåì, èíà÷å ñòðîèì ïðîãíîç
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if (t < x.size()) {
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e = x[t]-forecast[t];
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} else {
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e = 0;
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}
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S.push_back(S[t] + phi * T[t] + alpha * e); // óðîâåíü
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T.push_back(phi * T[t] + alpha * gamma * e); // çàòóõàþùèé òðåíä
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forecast.push_back(S[t+1] + phi * T[t+1]); // ïðîãíîç
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}
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}
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// ñôîðìèðîâàòü ìîäåëü
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void DATrendNoSeasonality::createModelForEstimation() {
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this->init(); // èíèöèàëèçèðîâàòü ìîäåëü
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double e = 0;
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//âûïîëíÿåòñÿ ïðîõîä ìîäåëè ïî ñãëàæèâàíèþ è ïðîãíîçèðîâàíèþ countPointForecast òî÷åê
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for (unsigned int t = 0; t < xLearning.size()-1 + this->countPointForecast; t++) {
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// ïîêà íå äîøëè äî êîíöà ðÿäà - ñãëàæèâàåì, èíà÷å ñòðîèì ïðîãíîç
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if (t < xLearning.size()) {
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e = xLearning[t]-forecast[t];
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} else {
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e = 0;
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}
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S.push_back(S[t] + phi * T[t] + alpha * e); // óðîâåíü
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T.push_back(phi * T[t] + alpha * gamma * e); // çàòóõàþùèé òðåíä
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forecast.push_back(S[t+1] + phi * T[t+1]); // ïðîãíîç
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}
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}
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// ìåòîä ïîëó÷åíèÿ ïðîãíîçà
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vector<double> DATrendNoSeasonality::getForecast() {
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vector<double> result;
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for (unsigned int i = forecast.size() - countPointForecast; i < forecast.size(); i++) {
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result.push_back(forecast[i]);
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}
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return result;
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}
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// ìåòîä ïîëó÷åíèÿ îöåíêè ìîäåëè
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double DATrendNoSeasonality::calcEstimation(Aic *aic) {
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return aic->getValue(2, this->xEstimation, this->forecast);
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}
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// ìåòîä ïîëó÷åíèÿ îïòèìèçèðîâàííîãî çíà÷åíèÿ îäíîãî ïàðàìåòðà
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// TODO: ðåàëèçîâàòü
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Param* DATrendNoSeasonality::optimize(Estimation *est) {
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Param *optimal = new Param();
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double minSmape = 99999;
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for (double al = 0.1; al < 1; al+= 0.05) {
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for (double gam = 0.1; gam < 1; gam+= 0.05) {
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for (double ph = 0.1; ph < 1; ph+= 0.05) {
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this->setParam("alpha", al);
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this->setParam("gamma", gam);
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this->setParam("phi", ph);
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this->createModelForEstimation();
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double smapeValue = est->getValue(getXEstimation(), getForecast());
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if (minSmape > smapeValue) {
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minSmape = smapeValue;
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optimal->alpha = al;
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optimal->gamma = gam;
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optimal->phi = phi;
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}
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}
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}
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}
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return optimal;
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}
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