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MENTAL MODEL · M11359

Moving Average Method

Moving Average Method
BusinessHigh supportFinance
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Updated 2026-08-13

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INTRODUCTION

English translation pending.

CORE DEFINITION

The moving average method estimates the current level of a series by taking the arithmetic mean of the last N observations, and it is used to smooth fluctuations, identify trends, and make short-range forecasts in finance, inventory, and traffic analysis. The core claim is that averaging a window suppresses random noise while preserving the underlying direction, so the signal becomes legible. The key condition is window size: a small N stays close to the data and reacts quickly, a large N removes more noise but lags behind turns, so the choice is a deliberate trade.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Window sizing: choose N to match how fast the underlying signal genuinely moves. - Noise stripping: replace each raw point with its window mean to see the trend clearly. - Sensitivity tuning: widen or narrow N deliberately rather than accepting a default.

anchor

Anchor fast decisions

Random fluctuations are equally likely to push the series up or down in any single period, so summing over N periods lets those independent errors cancel each other, while the true level they surround survives the averaging. A larger window cancels more errors but also dilutes real movement, which is why smoothness and lag rise together. The smoothed line is therefore a bias-variance trade in visible form, not a more accurate measurement of the latest instant.

MINIMUM ACTION

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Source support: Explicit

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    en.wikipedia.orghttps://en.wikipedia.org/wiki/Moving_averageZH · Explicit
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