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Modeling and Inferencing of Activity Profiles of Terrorist Groups
Published in Natalie M. Scala, James P. Howard, Handbook of Military and Defense Operations Research, 2020
as the metric. Note that the SMAPE score captures the relative error in prediction and is a number between 0% and 100% with a smaller value indicating a better prediction algorithm. An estimation error of the form ΔTi−Δ˜Ti only captures the absolute deviation between the true quantity and estimated quantity. On the other hand, a metric such as in (16.24) normalizes this absolute deviation by the weight of the true quantity itself and is thus a better reflection of the predictive power of the algorithm.
Optimized Haar wavelet-based blood cell image denoising with improved multiverse optimization algorithm
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
M. Mohana Dhas, N. Suresh Singh
Mean Square Error (MSE) is utilised to compute the average of squared errors between the predicted and actual values. The expected value of squared error loss is related to the risk function. Because pixels are randomly generated, the MSE is never 0 and is always strictly positive. In order to get a more precise estimate, the estimator takes out one or more pertinent factors. Structural Pearson Coefficient (PC) is calculated using the linear correlation between two sets of data. It is the combination of two variables with different standard deviations that are not covariant. The mean of the product of mean-adjusted random variables is represented by a product moment. Symmetric Mean Absolute Percentage Error (SMAPE) is a percentage or relative error-based accuracy measure. The absolute error is divided by the precise value’s magnitude to get the relative error. SMAPE is both lower and upper bound, unlike the mean absolute percentage error. Root Mean Square Error (RMSE) is used to calculate the average of error squares. It is calculated as the sum of the squares of the estimated and actual values. The relationship between the expected value of squared error loss and the risk function is there. Because pixels are random, RMSE is never 0 and is always strictly positive. To get a more precise estimate, the estimator excludes one or more pertinent factors.
Feature-based intermittent demand forecast combinations: accuracy and inventory implications
Published in International Journal of Production Research, 2022
Li Li, Yanfei Kang, Fotios Petropoulos, Feng Li
In previous studies, various forecasting evaluation metrics have been used for intermittent demand. The chosen metric of forecast errors may influence the ranked performance of the forecasting methods. Silver, Pyke, and Peterson (1998) pointed out that no single metric was universally best. Wallström and Segerstedt (2010) discussed a series of forecasting error measurements, especially for intermittent demand and split them into two categories, traditional (accuracy) and bias error measurements. As traditional measures, Wallström and Segerstedt (2010) considered mean absolute deviation (MAD), mean square error (MSE), symmetric Mean Absolute Percentage Error (sMAPE). As bias error measures, they examined the Cumulated Forecast Error (CFE), Number Of Shortages (NOS), and Periods In Stock (PIS). Kourentzes (2014) evaluated model selection results based on two accuracy metrics. The first is the Mean Absolute Scaled Error (MASE), which was suggested to be the standard measure for the data with different scales and zero values (Hyndman and Koehler 2006). The second is the scaled Absolute Periods In Stock (sAPIS), which is a scale-independent variant of PIS.
Individuals and their interactions in demand planning processes: an agent-based, computational testbed
Published in International Journal of Production Research, 2018
Jonas Hauke, Iris Lorscheid, Matthias Meyer
The computational testbed is applicable for evaluating planning performance in terms of accuracy, the emerging interactions among individuals and the robustness of the results to varying circumstances. A key performance indicator for demand planning is forecast accuracy, which is the evaluation measure in this study. We use the symmetric mean average percentage error (SMAPE), as it is used by our case study company as well as applied by another study of demand uncertainty in the semiconductor industry (Knoblich, Heavey, and Williams 2015). The SMAPE is appropriate to measure errors due to under- and over-forecasting. The measure is defined as the average of absolute errors divided by the sum of the actual (At) and forecasted value (Ft) over (p) periods: