在Java中,我们可以使用季节性分解的时间序列模型(例如Holt-Winters方法)来进行季度数据的预测。这里是一个简单的示例,展示了如何使用Apache Commons Math库进行季度数据预测:
<groupId>org.apache.commons</groupId>
<artifactId>commons-math3</artifactId>
<version>3.6.1</version>
</dependency>
import org.apache.commons.math3.analysis.interpolation.SplineInterpolator;
import org.apache.commons.math3.analysis.polynomials.PolynomialCurveFitter;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.fitting.WeightedObservedPoints;
import org.apache.commons.math3.stat.regression.SimpleRegression;
import org.apache.commons.math3.util.MathUtils;
import java.util.Arrays;
public class QuarterlyDataPrediction {
public static double[] predictQuarterlyData(double[] data, int forecastHorizon) {
int n = data.length;
int k = n / 4; // 假设每季度有k个数据点
// 对每个季度的数据进行插值
SplineInterpolator interpolator = new SplineInterpolator();
PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2);
ParametricUnivariateFunction function = new ParametricUnivariateFunction() {
@Override
public double value(double x, double... parameters) {
return fitter.value(x, parameters);
}
@Override
public double[] gradient(double x, double... parameters) {
return fitter.gradient(x, parameters);
}
};
double[] predictedData = new double[forecastHorizon];
for (int i = 0; i < 4; i++) {
WeightedObservedPoints points = new WeightedObservedPoints();
for (int j = 0; j < k; j++) {
points.add(j, data[i * k + j]);
}
double[] parameters = fitter.fit(points.toList());
for (int j = 0; j< forecastHorizon; j++) {
predictedData[j] += function.value(j + k, parameters);
}
}
// 对预测值进行归一化处理
double sum = 0;
for (double value : predictedData) {
sum += value;
}
for (int i = 0; i< forecastHorizon; i++) {
predictedData[i] /= sum;
}
return predictedData;
}
public static void main(String[] args) {
double[] data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; // 季度数据
int forecastHorizon = 4; // 预测未来4个季度的数据
double[] predictedData = predictQuarterlyData(data, forecastHorizon);
System.out.println("Predicted data: " + Arrays.toString(predictedData));
}
}
这个示例中,我们使用了一个简单的多项式拟合方法来预测季度数据。你可以根据实际需求修改这个方法,以适应更复杂的数据预测场景。