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Related Concept Videos

Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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A new accuracy measure based on bounded relative error for time series forecasting.

Chao Chen1, Jamie Twycross1, Jonathan M Garibaldi1

  • 1School of Computer Science, University of Nottingham, Nottingham, United Kingdom.

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|March 25, 2017
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A new accuracy measure, Unscaled Mean Bounded Relative Absolute Error (UMBRAE), is proposed for time series forecasting. UMBRAE addresses issues like outlier sensitivity and scale dependence, offering a robust alternative for evaluating forecasting methods.

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Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • Existing time series forecasting accuracy measures often exhibit limitations.
  • Issues include poor resistance to outliers and scale dependence, hindering reliable comparisons.
  • Symmetric mean absolute percentage error is commonly used but has drawbacks.

Purpose of the Study:

  • To introduce a novel accuracy measure for time series forecasting.
  • To address the shortcomings of existing measures, particularly outlier sensitivity and scale dependence.
  • To provide a robust and flexible tool for evaluating forecasting model performance.

Main Methods:

  • A comprehensive review of commonly used accuracy measures was conducted.
  • A new accuracy measure, Unscaled Mean Bounded Relative Absolute Error (UMBRAE), was developed.
  • Comparative evaluation using synthetic and real-world time series data was performed.

Main Results:

  • The proposed UMBRAE measure demonstrates robust performance across various criteria.
  • UMBRAE offers a user-selectable benchmark for flexible evaluation.
  • Performance was comparable or superior to existing measures in comparative tests.

Conclusions:

  • UMBRAE is a promising new metric for time series forecasting accuracy assessment.
  • It effectively mitigates common issues found in other measures.
  • UMBRAE is recommended, especially when geometric mean-based relative error measures are preferred.