IDEAS home Printed from https://ideas.repec.org/a/kap/iaecre/v15y2009i4p409-42010.1007-s11294-009-9233-8.html
   My bibliography  Save this article

Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series

Author

Listed:
  • Louie Ren
  • Yong Glasure

Abstract

Commonly used Mean Absolute Percentage Errors (MAPE) and the authors’ revised Mean Absolute Percentage Errors (RMAPE) are applied to measure the forecasting accuracy from different Moving Average Methods for independent time series. Simulation results show that both MAPE and RMAPE can only provide sensitive forecasting accuracy measurements on Moving Average Methods when coefficients of variation (c.v.) are smaller than 0.4 or is much greater than 4.0 for those independent time series. For independent time series with moderate c.v.’s, the complexity from the ratios of MAPE and RMAPE will mislead researchers on distinguishing the forecasting accuracies from different Moving Average Methods. The complexity from the ratios will be released only when the c.v. is very small, or when the c.v. is very large. Therefore, when data are from independent time series, the Mean Absolute Deviation (MAD) reveals valid the forecasting accuracies from various Moving Average Methods, but not from MAPE or RMAPE. Copyright International Atlantic Economic Society 2009

Suggested Citation

  • Louie Ren & Yong Glasure, 2009. "Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(4), pages 409-420, November.
    • Handle: RePEc:kap:iaecre:v:15:y:2009:i:4:p:409-420:10.1007/s11294-009-9233-8
    DOI: 10.1007/s11294-009-9233-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11294-009-9233-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11294-009-9233-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    2. Chatfield, Chris, 1988. "Apples, oranges and mean square error," International Journal of Forecasting, Elsevier, vol. 4(4), pages 515-518.
    3. Weller, Barry R & Kurre, James A, 1987. "Applicability of the Transfer Function Approach to Forecasting Employment Levels in Small Regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 21(1), pages 34-43, March.
    4. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    5. David Swanson & Jeff Tayman & Charles Barr, 2000. "A note on the measurement of accuracy for subnational demographic estimates," Demography, Springer;Population Association of America (PAA), vol. 37(2), pages 193-201, May.
    6. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    7. Weller, Barry R., 1989. "National indicator series as quantitative predictors of small region monthly employment levels," International Journal of Forecasting, Elsevier, vol. 5(2), pages 241-247.
    8. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jing Huang & Jinle Kang & Huimin Wang & Zhiqiang Wang & Tian Qiu, 2020. "A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
    2. Tascón, Diana C. & Díaz Olariaga, Oscar, 2021. "Air traffic forecast and its impact on runway capacity. A System Dynamics approach," Journal of Air Transport Management, Elsevier, vol. 90(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:kap:iaecre:v:15:y:2009:i:4:p:409-420 is not listed on IDEAS
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    5. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    6. Bunn, Derek W. & Taylor, James W., 2001. "Setting accuracy targets for short-term judgemental sales forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 159-169.
    7. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    8. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    9. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    10. McKenzie, Jordi, 2011. "Mean absolute percentage error and bias in economic forecasting," Economics Letters, Elsevier, vol. 113(3), pages 259-262.
    11. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    12. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).
    13. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    14. Dominik Martin & Philipp Spitzer & Niklas Kuhl, 2020. "A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs," Papers 2004.10537, arXiv.org.
    15. Che-Yu Hung & Chien-Chih Wang & Shi-Woei Lin & Bernard C. Jiang, 2022. "An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    16. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
    17. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
    18. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    19. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    20. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean Marie, 2005. "A short and mean-term automatic forecasting system--application to textile logistics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 275-284, February.
    21. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.

    More about this item

    Keywords

    Mean Absolute Percentage Errors (MAPE); Revised Mean Absolute Percentage Errors (RMAPE); Forecasting accuracy; Coefficient of variation (c.v.); Mean Absolute Deviation (MAD); C10 Econometrics; Statistics; M21 Managerial Economics; M00 Business Administration;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:iaecre:v:15:y:2009:i:4:p:409-420:10.1007/s11294-009-9233-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.