Data, Inference, and Decisions
Mean absolute error (MAE) is a measure of the average magnitude of errors between predicted values and actual values, without considering their direction. It is calculated as the average of the absolute differences between each predicted and actual value, making it useful in assessing model performance. In the context of time series analysis, MAE can help evaluate how well a model captures components like trend, seasonality, and cycles by quantifying discrepancies in forecasts.
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