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http://dx.doi.org/10.5855/ENERGY.2018.27.4.103

Estimation Method of Predicted Time Series Data Based on Absolute Maximum Value  

Shin, Ki-Hoon (NKIA Inc.)
Kim, Chul (Dept. of Computer Science, Hanyang University)
Nam, Sang-Hun (NKIA Inc.)
Park, Sung-Jae (NKIA Inc.)
Yoo, Sung-Soo (NKIA Inc.)
Publication Information
Journal of Energy Engineering / v.27, no.4, 2018 , pp. 103-110 More about this Journal
Abstract
In this paper, we introduce evaluation method of time series prediction model with new approach of Mean Absolute Percentage Error(hereafter MAPE) and Symmetric Mean Absolute Percentage Error(hereafter sMAPE). There are some problems using MAPE and sMAPE. First MAPE can't evaluate Zero observation of dataset. Moreover, when the observed value is very close to zero it evaluate heavier than other methods. Finally it evaluate different measure even same error between observations and predicted values. And sMAPE does different evaluations are made depending on whether the same error value is over-predicted or under-predicted. And it has different measurement according to the each sign, even if error is the same distance. These problems were solved by Maximum Mean Absolute Percentage Error(hereafter mMAPE). we used the absolute maximum of observed value as denominator instead of the observed value in MAPE, when the value is less than 1, removed denominator then solved the problem that the zero value is not defined. and were able to prevent heavier measurement problem. Also, if the absolute maximum of observed value is greater than 1, the evaluation values of mMAPE were compared with those of the other evaluations. With Beijing PM2.5 temperature data and our simulation data, we compared the evaluation values of mMAPE with other evaluations. And we proved that mMAPE can solve the problems that we mentioned.
Keywords
MAPE; sMAPE; forecast; time series evaluation; anomaly detection;
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1 Arnaud de Myttenaer et al., 2016, Mean Absolute Percentage Error for regression models, Neurocomputing, Vol. 192, pp. 38-48   DOI
2 Fumiya Akashi, Shuyang Bai, Murad S. Taqqu, 2018, Robust regression on stationary time series: a self-normalized resampling approach., JOURNAL OF TIME SERIESANALYSIS, 39, pp. 417-432   DOI
3 Rob J Hyndman, George Athanasopoulos, 2014, Forecasting : principles and practice, OTexts, Heathmont, Vic, p. 291
4 Rob J Hyndman, Anne B Koehler, 2006, Another look at measures of forecast accuracy, International journal of forecasting, 22(4), pp. 679-688   DOI
5 Ji, Y.M., Yoo, J.J., 2017, Intelligent IoT based building automatic control system, Magazine of the SAREK, Vol. 46, No. 7, pp. 32-40
6 Kim, S.I., Kim, H.Y., 2016, A new metric of absolute percentage error for intermittent demand forecasts, International Journal of Forecasting, Vol. 32, No. 3, pp. 669-679   DOI
7 Peter J. Huber, 1964, Robust Estimation of a Location Parameter, The Annals of Mathematical Statistics, Vol. 35, No. 1, pp. 73-101   DOI
8 Spyros Makridakis, 1993, Accuracy measures: theoretical and practical concerns, International Journal of Forecasting, Vol. 9, pp. 527-529   DOI
9 Spyros Makridakis, Michele Hibon, 1997, ARMA Models and the Box-Jenkins Methodology, Journal of forecasting, Vol. 16, pp. 147-163   DOI
10 Xuan Liang et al, 2015, Assessing Beijing's PM2.5 pollution: severity, weather impact, APEC and winter heating, Proc. R. Soc., Vol. 471, No. 2181, http://rspa.royalsocietypublishing.org/content/471/2182/20150257
11 J. Scott Armstrong, Fred L. Collopy, 1992, Error measures for generalizing about forecasting methods: Empirical comparisons, International Journal of Forecasting, 8, pp. 69-80   DOI