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ts-aggregator/project_template/MultTrendMultSeasonality.cpp

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2022-12-13 12:36:06 +04:00
//
// <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>:
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD>, <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
//
#include "StdAfx.h"
#include <iostream>
#include "MultTrendMultSeasonality.h"
#include <math.h>
#include "Param.h"
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
MultTrendMultSeasonality::MultTrendMultSeasonality(vector<double> timeSeries, int countPointForecast) {
this->x = timeSeries;
this->countPointForecast = countPointForecast;
this->partition();
}
MultTrendMultSeasonality::~MultTrendMultSeasonality() {
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
std::vector<double> ().swap(S);
std::vector<double> ().swap(x);
std::vector<double> ().swap(T);
std::vector<double> ().swap(I);
std::vector<double> ().swap(forecast);
}
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>, <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
void MultTrendMultSeasonality::init() {
S.clear();
T.clear();
I.clear();
forecast.clear();
double sumS = 0;
double sumT = 0;
for (unsigned int t = 0; t < p; t++) {
sumS += x[t];
sumT += x[t+p];
}
S.push_back(sumS / p);
T.push_back((sumT/ p - S[0]) / p);
for (unsigned int t = 0; t < p; t++) {
I.push_back(x[t] / S[0]);
}
forecast.push_back(S[0] * T[0] * I[0]);
}
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
void MultTrendMultSeasonality::setParam(string paramName, double value) {
if (paramName.compare("alpha") == 0) {
this->alpha = value;
} else if (paramName.compare("gamma") == 0) {
this->gamma = value;
} else if (paramName.compare("p") == 0) {
this->p = value;
} else if (paramName.compare("delta") == 0) {
this->delta = value;
}
}
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
void MultTrendMultSeasonality::createModel() {
this->init(); // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
double e = 0;
//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> countPointForecast <20><><EFBFBD><EFBFBD><EFBFBD>
for (unsigned int t = 0; t < x.size()-1 + this->countPointForecast; t++) {
// <20><><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD> - <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>, <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
if (t < x.size()) {
e = x[t]-forecast[t];
} else {
e = 0;
}
S.push_back(S[t] * T[t] + alpha * e / I[t]); // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
T.push_back(T[t] + alpha * gamma * e / (I[t] * S[t])); // <20><><EFBFBD><EFBFBD><EFBFBD>
I.push_back(I[t] + delta * e / (S[t] * T[t])); // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
forecast.push_back(S[t+1] * T[t+1] * I[t+1]); // <09><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
}
}
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>. <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD>
void MultTrendMultSeasonality::createModelForEstimation() {
this->init(); // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
double e = 0;
//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> countPointForecast <20><><EFBFBD><EFBFBD><EFBFBD>
for (unsigned int t = 0; t < xLearning.size()-1 + this->countPointForecast; t++) {
// <20><><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD> - <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>, <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
if (t < xLearning.size()) {
e = xLearning[t]-forecast[t];
} else {
e = 0;
}
S.push_back(S[t] * T[t] + alpha * e / I[t]); // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
T.push_back(T[t] + alpha * gamma * e / (I[t] * S[t])); // <20><><EFBFBD><EFBFBD><EFBFBD>
I.push_back(I[t] + delta * e / (S[t] * T[t])); // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
forecast.push_back(S[t+1] * T[t+1] * I[t+1]); // <09><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
}
}
// <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
vector<double> MultTrendMultSeasonality::getForecast() {
vector<double> result;
for (unsigned int i = forecast.size() - countPointForecast; i < forecast.size(); i++) {
result.push_back(forecast[i]);
}
return result;
}
// <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
double MultTrendMultSeasonality::calcEstimation(Aic *aic) {
return aic->getValue(3, this->xEstimation, this->forecast);
}
// <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
// TODO: <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
Param* MultTrendMultSeasonality::optimize(Estimation *est) {
Param *optimal = new Param();
double minSmape = 99999;
for (double al = 0.1; al < 1; al+= 0.01) {
for (double gam = 0.1; gam < 1; gam+= 0.01) {
for (double del = 0.1; del < 1;del+= 0.01) {
this->setParam("alpha", al);
this->setParam("gamma", gam);
this->setParam("delta", del);
this->createModelForEstimation();
double smapeValue = est->getValue(getXEstimation(), getForecast());
if (minSmape > smapeValue) {
minSmape = smapeValue;
optimal->alpha = al;
optimal->gamma = gam;
optimal->delta = del;
}
}
}
}
return optimal;
}
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