平滑指数预测

案例:

a—0.3

预测未来期的值的计算公式为：

其中：

案例:

        a–0.3

5.代码实现

public class First {

public static void main(String[] args) {
List

list = new LinkedList

();

list.add(133d);
list.add(88d);
list.add(150d);
list.add(123d);
list.add(404d);
list.add(107d);
list.add(674d);
list.add(403d);
list.add(243d);
list.add(257d);
System.out.println(getFirst(list,0.3));
}

//一次指数平滑
public static Double getFirst(List

list,Double module){

Double first=(list.get(0)+list.get(1)+list.get(2))/3;
double sum=0d;
for(int i=0;i Double temp=list.get(i);

first=module*temp+(1-module)*first;
if(i sum+=Math.abs(temp-first);

}
System.out.println(“预测值:”+first+”误差值:”+(temp-first));
}
System.out.println(“均方差:”+sum/(list.size()-1));
return first;
}

/
* 二次指数平滑法求预测值 预测线性趋势
* 
* @param list
*            基础数据集合
* @param year
*            未来第几期
* @param modulus
*            平滑系数
* @return 预测值
*/
private static Double getExpect(List

list, int year, Double modulus) {

if (list.size() < 3 || modulus <= 0 || modulus >= 1) {
return null;
}
Double sum=0d;
Double modulusLeft = 1 – modulus;
// 一次预测 >20项默认第一个值为初始值
Double lastIndex = (list.get(0)+list.get(1)+list.get(2))/3;
// 二次预测 >20项默认第一个值为初始值
Double lastSecIndex = (list.get(0)+list.get(1)+list.get(2))/3;
for (Double data : list) {
// st1
lastIndex = modulus * data + modulusLeft * lastIndex;
// st2
lastSecIndex = modulus * lastIndex + modulusLeft * lastSecIndex;
// AT
Double a = 2 * lastIndex – lastSecIndex;
// BT
Double b = (modulus / modulusLeft) * (lastIndex – lastSecIndex);
sum+=Math.abs(data-(a + b * year));
System.out.println(“预测值:”+(a + b * year)+”误差值:”+(data-(a + b * year)));
}
System.out.println(“平均绝对误差:”+(sum/list.size()));
// AT
Double a = 2 * lastIndex – lastSecIndex;
// BT
Double b = (modulus / modulusLeft) * (lastIndex – lastSecIndex);
return a + b * year;
}

//三次指数平滑
public static Double getThridExpect(List

list, int year, Double modulus) {

if (list.size() < 3 || modulus <= 0 || modulus >= 1) {
return null;
}
Double sum=0d;
Double modulusLeft = 1 – modulus;
// 一次预测 >20项默认第一个值为初始值
Double lastIndex = (list.get(0)+list.get(1)+list.get(2))/3;
// 二次预测 >20项默认第一个值为初始值
Double lastSecIndex = (list.get(0)+list.get(1)+list.get(2))/3;
// 三次预测 >20项默认第一个为初始值
Double lastThreadIndes = (list.get(0)+list.get(1)+list.get(2))/3;
for (Double data : list) {
// st1
lastIndex = modulus * data + modulusLeft * lastIndex;
// st2
lastSecIndex = modulus * lastIndex + modulusLeft * lastSecIndex;
// st3
lastThreadIndes = modulus * lastSecIndex + modulusLeft * lastThreadIndes;

// AT
Double a = 3 * lastIndex – 3 * lastSecIndex + lastThreadIndes;
// BT
Double b = (modulus /( 2 * (1 – modulus) * (1 – modulus))) * ((6 – 5 * modulus) * lastIndex
– 2 * (5 – 4 * modulus) * lastSecIndex + (4 – 3 * modulus) * lastThreadIndes);
// CT
Double c = ((modulus * modulus) / (2 * (1 – modulus) * (1 – modulus)))
* (lastIndex – 2 * lastSecIndex + lastThreadIndes);
sum+=Math.abs(data-(a + b * year + c * year * year));
System.out.println(“预测值:”+(a + b * year + c * year * year)+”误差值:”+(data-(a + b * year + c * year * year)));

}
System.out.println(“平均绝对误差:”+(sum/list.size()));
// AT
Double a = 3 * lastIndex – 3 * lastSecIndex + lastThreadIndes;
// BT
Double b = (modulus /( 2 * (1 – modulus) * (1 – modulus))) * ((6 – 5 * modulus) * lastIndex
– 2 * (5 – 4 * modulus) * lastSecIndex + (4 – 3 * modulus) * lastThreadIndes);
// CT
Double c = ((modulus * modulus) / (2 * (1 – modulus) * (1 – modulus)))
* (lastIndex – 2 * lastSecIndex + lastThreadIndes);
return a + b * year + c * year * year;
}
}