MAE analysis technique?

<p id="isPasted">The Mean Absolute Error (MAE) analysis technique is a widely used metric in data analysis, machine learning, and forecasting to measure the average magnitude of the errors between predicted values and actual observed values.&nbsp;</p><p>A lower MAE score indicates better model performance.&nbsp;</p><p><strong>Key Features</strong></p><p>MAE is easy to understand as its error value uses the same units as the data. It is a linear score that weights all individual differences equally and is less affected by outliers compared to Mean Squared Error. However, MAE does not indicate whether a prediction over- or under-estimated the actual value.&nbsp;</p><p id="isPasted"><strong>Calculation Formula&nbsp;</strong></p><p>The formula for calculating MAE is the sum of the absolute differences between actual values (y sub i) and predicted values (ŷ sub i), divided by the total number of observations (n):&nbsp;</p><p class="forexqa-img-container"><img draggable="false" src="https://prod-forexqna.s3.amazonaws.com/uploads/froala_editor/images/1766551874134.gif" style="height: 1339.1px; width: 408px; left: 0px; top: 0px;" data-sae="" id="isPasted" class="fr-fil fr-dib fr-draggable forexqa-img"><strong>Applications</strong></p><p>MAE is applied in various fields:</p><p>Forecasting: Evaluating the accuracy of predictions in time series analysis for areas like stock prices and sales.</p><p>Machine Learning: Used as a loss function for regression models.</p><p>Risk Management (Trading): A related concept, Maximum Adverse Excursion (MAE), helps traders measure the largest loss in a trade to inform stop-loss placement and risk management.&nbsp;</p><p><br></p><p><br></p><p><br></p>