A new metric of absolute percentage error for intermittent demand forecasts

By Sungil Kim, Heeyoung Kim. 2016

1,056 citations recorded by the source corpus.

The mean absolute percentage error (MAPE) is one of the most widely used measures of forecast accuracy, due to its advantages of scale-independency and interpretability. However, MAPE has the significant disadvantage that it produces infinite or undefined values for zero or close-to-zero actual values. In order to address this issue in MAPE, we propose a new measure of forecast accuracy called the mean arctangent absolute percentage error (MAAPE). MAAPE has been developed through looking at MAPE from a different angle. In essence, MAAPE is a slope as an angle, while MAPE is a slope as a ratio, considering a triangle with adjacent and opposite sides that are equal to an actual value and the difference between the actual and forecast values, respectively. MAAPE inherently preserves the philosophy of MAPE, overcoming the problem of division by zero by using bounded influences for outliers in a fundamental manner through considering the ratio as an angle instead of a slope. The theoretical properties of MAAPE are investigated, and the practical advantages are demonstrated using both simulated and real-life data.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Chongqing Kang
2. Eva Boj del Val
3. Yi Wang
4. Qixin Chen
5. Alexei Botchkarev
6. Tao Hong
7. Ansari Saleh Ahmar

In Electrical and Electronic Engineering

1. Chongqing Kang
2. Qixin Chen
3. Yi Wang
4. Eva Boj del Val
5. Alexei Botchkarev
6. Tao Hong
7. Ansari Saleh Ahmar
8. Rafal Weron

In Management Science and Operations Research

1. Chongqing Kang
2. Yi Wang
3. Qixin Chen
4. Eva Boj del Val
5. Reza Alizadeh
6. Alexei Botchkarev
7. Farrokh Mistree
8. Janet K. Allen

Citations in the corpus

• Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology (2018, background): “For example, mean arctangent absolute percentage error (MAAPE) proposed by Kim and Kim (2016).”
• SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain (2020, positive): “For the evaluation of the forecasting methods, we applied two forecasting accuracy measures, including mean absolute percentage error (MAPE) (Kim and Kim, 2016).”
• Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, background): “The mean arctangent absolute percentage error (MAAPE) was proposed in [126] to consider the intermittent nature of individual load”
• Managing computational complexity using surrogate models: a critical review (2020, background): “To address the issue of infinite values MAAPE has been developed which is another version of MAPE (Kim and Kim 2016).”