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On time series data and optimal parameters

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  • Rasmussen, Rasmus

Abstract

Forecasting using time series (TS) models are often based on linear regression or methods using various smoothing techniques. When estimating the parameters used in smoothing techniques, it has become a common practice to optimize the smoothing constants (parameters). This new practice is a result of the ease such methods can be accomplished when using the built in Solver optimization tool in modern spreadsheets. However, the capabilities of Solver can be utilized further to optimize more of the parameters, particularly the initial or starting parameters. This paper presents examples of exponential smoothing techniques, demonstrating improved fits when adopting this idea of optimizing the initial parameters as well as the smoothing constants. It also demonstrates that linear regression is a special case of Holt's exponential smoothing model with trend. Normalization of the seasonal parameters in models incorporating seasonality is also discussed, showing improved fits to TS data. Educators are encouraged to adopt the idea of letting Solver optimize more of the parameters than what is common practice today, in other models and in other fields.

Suggested Citation

  • Rasmussen, Rasmus, 2004. "On time series data and optimal parameters," Omega, Elsevier, vol. 32(2), pages 111-120, April.
    • Handle: RePEc:eee:jomega:v:32:y:2004:i:2:p:111-120
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    File URL: http://www.sciencedirect.com/science/article/pii/S0305-0483(03)00113-0
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    References listed on IDEAS

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    1. Ragsdale, Cliff T. & Plane, Donald R., 2000. "On modeling time series data using spreadsheets," Omega, Elsevier, vol. 28(2), pages 215-221, April.
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    Cited by:

    1. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    2. Wallström, Peter & Segerstedt, Anders, 2010. "Evaluation of forecasting error measurements and techniques for intermittent demand," International Journal of Production Economics, Elsevier, vol. 128(2), pages 625-636, December.
    3. Taylor, James W. & Snyder, Ralph D., 2012. "Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing," Omega, Elsevier, vol. 40(6), pages 748-757.
    4. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    5. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    6. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    7. Oscar Trull & Juan Carlos García-Díaz & Alicia Troncoso, 2020. "Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models," Mathematics, MDPI, vol. 8(2), pages 1-16, February.
    8. Lawson, Barry R. & Baker, Kenneth R. & Powell, Stephen G. & Foster-Johnson, Lynn, 2009. "A comparison of spreadsheet users with different levels of experience," Omega, Elsevier, vol. 37(3), pages 579-590, June.

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