java 41 lines · 7 steps

A rolling average over a sliding window

Maintain the mean of the last N readings in O(1) per update by tracking a running sum and a bounded deque.

Explained by highlit
Intermediate sliding-window running-sum deque incremental-computation
1import java.util.ArrayDeque;
2import java.util.Deque;
3 
4public final class RollingAverage {
5 
6 private final int windowSize;
7 private final Deque<Double> window = new ArrayDeque<>();
8 private double sum;
9 
10 public RollingAverage(int windowSize) {
11 if (windowSize <= 0) {
12 throw new IllegalArgumentException("windowSize must be positive");
13 }
14 this.windowSize = windowSize;
15 }
16 
17 public double add(double reading) {
18 window.addLast(reading);
19 sum += reading;
20 if (window.size() > windowSize) {
21 sum -= window.removeFirst();
22 }
23 return average();
24 }
25 
26 public double average() {
27 if (window.isEmpty()) {
28 return Double.NaN;
29 }
30 return sum / window.size();
31 }
32 
33 public boolean isFull() {
34 return window.size() == windowSize;
35 }
36 
37 public void reset() {
38 window.clear();
39 sum = 0.0;
40 }
41}
01 / 01
STEP 01

Walkthrough

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Three takeaways
  1. 1Keeping a running sum turns each average update into constant-time work instead of re-summing the window.
  2. 2A bounded deque models a sliding window cleanly: append at one end, evict at the other.
  3. 3Guard edge cases explicitly — reject bad sizes early and return NaN when there's no data to average.

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