Stabilize sliding window anomaly score noisy edge lab
Medium Algorithms arena: two pointers, running sums, and window constraints
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Missing prerequisites: algorithms-easy-sliding-window-anomaly-edge-lab
The initial sliding window anomaly score solution worked on clean data, but production-like noise now exposes weak assumptions. You are working inside an algorithm arena, and the arena only clears when the result is safe, deterministic, and explainable.
Learn to apply two pointers, running sums, and window constraints in an algorithm arena while explaining the invariant, safety constraints, and hidden edge cases.
- Demonstrates two pointers, running sums, and window constraints
- Handles the visible sample and hidden edge cases
- Keeps output deterministic and explainable
- Avoids unsafe dynamic execution
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