IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/ws122519.html
   My bibliography  Save this paper

Seasonal modulation mixed models for time series forecasting

Author

Listed:
  • Lee, Dae-Jin
  • Durbán, María

Abstract

We propose an extension of a seasonal modulation smooth model with P-splines for times series data using a mixed model formulation. A smooth trend with seasonality decomposition can be estimated simultaneously. We extend the model to consider the forecasting of new future observations in the mixed model framework. Two different approaches are used for forecasting in the context of mixed models, and the equivalence of both methods is shown. The methodology is illustrated with monthly sulphur dioxide (SO2) levels in a selection of monitoring sites in Europe from January 1990 to December 2001.

Suggested Citation

  • Lee, Dae-Jin & Durbán, María, 2012. "Seasonal modulation mixed models for time series forecasting," DES - Working Papers. Statistics and Econometrics. WS ws122519, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws122519
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/5d6fc68e-8dc5-4be4-bfdc-c03f99f99550/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. I. D. Currie & M. Durban & P. H. C. Eilers, 2006. "Generalized linear array models with applications to multidimensional smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 259-280, April.
    2. 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.
    3. Gilmour, Arthur & Cullis, Brian & Welham, Sue & Gogel, Beverley & Thompson, Robin, 2004. "An efficient computing strategy for prediction in mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 571-586, January.
    4. Maria Durbán & Iain D. Currie, 2003. "A note on P-spline additive models with correlated errors," Computational Statistics, Springer, vol. 18(2), pages 251-262, July.
    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. Marra, Giampiero & Radice, Rosalba, 2017. "Bivariate copula additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 99-113.

    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. Lee, Dae-Jin & Durbán, María, 2009. "P-spline anova-type interaction models for spatio-temporal smoothing," DES - Working Papers. Statistics and Econometrics. WS ws093312, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Philipp F. M. Baumann & Enzo Rossi & Alexander Volkmann, 2020. "What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC," Papers 2006.06274, arXiv.org, revised Aug 2022.
    3. Alba Carballo & María Durbán & Dae-Jin Lee, 2021. "Out-of-Sample Prediction in Multidimensional P-Spline Models," Mathematics, MDPI, vol. 9(15), pages 1-23, July.
    4. Lee, Dae-Jin, 2019. "Out-of-sample prediction in multidimensional P-spline models," DES - Working Papers. Statistics and Econometrics. WS 28630, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    6. Zanin, Luca & Marra, Giampiero, 2012. "Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach," Economic Modelling, Elsevier, vol. 29(4), pages 1328-1337.
    7. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    8. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    9. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    10. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    11. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    12. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    13. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    14. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    15. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    16. Rivera, Nilza & Guzmán, Juan Ignacio & Jara, José Joaquín & Lagos, Gustavo, 2021. "Evaluation of econometric models of secondary refined copper supply," Resources Policy, Elsevier, vol. 73(C).
    17. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    18. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    19. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    20. Welham, S.J. & Thompson, R., 2009. "A note on bimodality in the log-likelihood function for penalized spline mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 920-931, February.
    21. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.

    More about this item

    Keywords

    Varying-coefficient models;

    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:cte:wsrepe:ws122519. 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: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    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.