如何在 RxJava 中计算移动平均值 [英] how to calculate moving average in RxJava

问题描述

在金融领域，我们通常需要从时间序列数据流中计算移动窗口聚合值，以移动平均为例，假设我们有以下数据流(T是时间戳，V是实际vlaue):

In finance domain, we usually need to calculate the moving-window aggregate value from a stream of time series data, use moving average as an example, say we have the following data stream(T is time stamp and V is the actual vlaue):

[T0,V0],[T1,V1],[T2,V2],[T3,V3],[T4,V4],[T5,V5],[T6,V6],[T7,V7],[T8,V8],[T9,V9],[T10,1V0],......

从我们得到的流中计算移动平均值 3:

to calculate a moving average 3 from the stream we get:

avg([T0,V0],[T1,V1],[T2,V2]),
avg([T1,V1],[T2,V2],[T3,V3]),
avg([T2,V2],[T3,V3],[T4,V4]),
avg([T3,V3],[T4,V4],[T5,V5]),
avg([T4,V4],[T5,V5],[T6,V6]),...

要计算移动平均线，我们似乎可以这样做:

To calculate the moving average, it seems like we could do it by :

1. 从原始流构建一个 Observable
2. 通过将值聚合到组中，从原始流构建一个 Observable
3. 使用聚合运算符计算步骤 2 中 Observable 的最终结果.

第 1 步和第 3 步实现起来很简单，但是，对于第 2 步，当前的 RxJava 似乎没有内置运算符来生成移动窗口组，window/groupBy 运算符似乎不适合这种情况，我没有找到一种简单的方法来组合现有运算符的解决方案，有人可以建议如何以优雅"的方式在 RxJava 中做到这一点吗?

Step 1 and 3 is trivial to implement, however, for step 2 it seems like current RxJava do not have build-in operator to produce moving-windows groups, the window/groupBy operator seems not fit in this case, and I did not find a easy way to compose a solution from existing operators, can any one suggest how to do this in RxJava in a "elegantly" fashion?

推荐答案

RxJava version: 0.15.1

RxJava version: 0.15.1

import java.util.List;                                                          
import rx.Observable;                                                           
import rx.util.functions.Action1;                                               
                                                                                
class Bar {                                                                     
                                                                                
    public static void main(String args[]) {                                    
                                                                                
        Integer arr[] = {1, 2, 3, 4, 5, 6}; // N = 6                            
        Observable<Integer> oi = Observable.from(arr);                          
                                                                                
        // 1.- bundle 3, skip 1                                                 
        oi.buffer(3, 1)                                                         
        /**                                                                     
         * 2.- take only the first X bundles                                    
         * When bundle 3, X = N - 2 => 4                                        
         * When bundle 4, X = N - 3 => 3                                        
         * When bundle a, X = N - (a-1)                                         
         */                                                                     
          .take(4)                                                              
        // 3.- calculate average                                                
          .subscribe(new Action1<List<Integer>>() {                             
            @Override                                                           
            public void call(List<Integer> lst) {                               
                int sum = 0;                                                    
                for(int i = 0; i < lst.size(); i++) {                           
                    sum += lst.get(i);                                          
                }                                                               
                                                                                
                System.out.println("MA(3) " + lst +                             
                                   " => " + sum / lst.size());                  
            }                                                                   
        });                                                                     
                                                                                
    }                                                                           
                                                                                
}  

示例输出:

MA(3) [1, 2, 3] =>2

MA(3) [1, 2, 3] => 2

MA(3) [2, 3, 4] =>3

MA(3) [2, 3, 4] => 3

MA(3) [3, 4, 5] =>4

MA(3) [3, 4, 5] => 4

MA(3) [4, 5, 6] =>5

MA(3) [4, 5, 6] => 5

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