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Minimum Sample Size requirements for Seasonal Forecasting Models

Author

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  • Rob J. Hyndman
  • Andrey V. Kostenko

Abstract

Authors Rob Hyndman and Andrey Kostenko discuss the bare minimum data requirements for fitting three common types of seasonal models: regression with seasonal dummies, exponential smoothing, and ARIMA. Achieving the requisite minimum numbers, however, does not ensure adequate estimates of seasonality. The amount of additional data required depends on the amount of noise (random variation) in the data. Unfortunately, there are no simple rules about sample size, and the authors note that published tables on sample size requirements are overly simplified. Copyright International Institute of Forecasters, 2007

Suggested Citation

  • Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    • Handle: RePEc:for:ijafaa:y:2007:i:6:p:12-15
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    6. Waseem Khan & Sana Fatima, 2016. "An Assessment of Sectoral Dynamics and Employment Shift in Indian and Chinese Economy," South Asian Survey, , vol. 23(2), pages 119-134, September.
    7. Dittmer, Celina & Krümpel, Johannes & Lemmer, Andreas, 2021. "Power demand forecasting for demand-driven energy production with biogas plants," Renewable Energy, Elsevier, vol. 163(C), pages 1871-1877.
    8. Chan, Man-pui Sally & Winneg, Kenneth & Hawkins, Lauren & Farhadloo, Mohsen & Jamieson, Kathleen Hall & Albarracín, Dolores, 2018. "Legacy and social media respectively influence risk perceptions and protective behaviors during emerging health threats: A multi-wave analysis of communications on Zika virus cases," Social Science & Medicine, Elsevier, vol. 212(C), pages 50-59.
    9. Gati Gayatri & I Gede Nyoman Mindra Jaya & Vience Mutiara Rumata, 2022. "The Indonesian Digital Workforce Gaps in 2021–2025," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    10. Anna Manowska & Anna Bluszcz, 2022. "Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network," Energies, MDPI, vol. 15(13), pages 1-23, July.
    11. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    12. Tomasz Śmiałkowski & Andrzej Czyżewski, 2022. "Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters," Energies, MDPI, vol. 15(24), pages 1-23, December.
    13. repec:jss:jstsof:27:i03 is not listed on IDEAS
    14. Jussim, Maxim, 2014. "Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich," Bayreuth Reports on Information Systems Management 57, University of Bayreuth, Chair of Information Systems Management.
    15. Mohammed Aminu Sualihu & M. Arifur Rahman, 2014. "Payment Behaviour of Electricity Consumers: Evidence from the Greater Accra Region of Ghana," Global Business Review, International Management Institute, vol. 15(3), pages 477-492, September.
    16. Pedro M. R. Bento & Jose A. N. Pombo & Maria R. A. Calado & Silvio J. P. S. Mariano, 2021. "Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting," Energies, MDPI, vol. 14(21), pages 1-21, November.
    17. Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    18. Nils Droste & Claudia Becker & Irene Ring & Rui Santos, 2018. "Decentralization Effects in Ecological Fiscal Transfers: A Bayesian Structural Time Series Analysis for Portugal," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(4), pages 1027-1051, December.
    19. Carmona-Benítez, Rafael Bernardo & Nieto, María Rosa, 2020. "SARIMA damp trend grey forecasting model for airline industry," Journal of Air Transport Management, Elsevier, vol. 82(C).
    20. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
    21. Vanella, Patrizio, 2016. "The Total Fertility Rate in Germany until 2040 - A Stochastic Principal Components Projection based on Age-specific Fertility Rates," Hannover Economic Papers (HEP) dp-579, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    22. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251.
    23. Hloušková, Z. & Ženíšková, P. & Prášilová, M., 2018. "Comparison of Agricultural Costs Prediction Approaches," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(1).
    24. Jiří Šindelář, 2019. "Sales forecasting in financial distribution: a comparison of quantitative forecasting methods," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(3), pages 69-80, December.

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