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 
  private final int windowSize;
  private final Deque<Double> window = new ArrayDeque<>();
  private double sum;
9 
  public RollingAverage(int windowSize) {
      if (windowSize <= 0) {
          throw new IllegalArgumentException("windowSize must be positive");
      }
      this.windowSize = windowSize;
  }
16 
  public double add(double reading) {
      window.addLast(reading);
      sum += reading;
      if (window.size() > windowSize) {
          sum -= window.removeFirst();
      }
      return average();
  }
25 
  public double average() {
      if (window.isEmpty()) {
          return Double.NaN;
      }
      return sum / window.size();
  }
32 
  public boolean isFull() {
      return window.size() == windowSize;
  }
36 
  public void reset() {
      window.clear();
      sum = 0.0;
  }
41}

01 / 01

STEP 01

Walkthrough

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Three takeaways

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

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