Root Mean Square Error (RMSE) is a metric used to measure the differences between values predicted by a model and the actual values observed. It is calculated by taking the square root of the average of the squares of the errors, providing a single measure of how well a model predicts data. This metric is particularly useful in assessing models related to time series, as it helps quantify the overall accuracy of predictions when accounting for components like trends, seasonality, and cycles.
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