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Evaluation Procedures for Forecasting with Spatiotemporal Data

Author

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  • Mariana Oliveira

    (Department of Computer Science, Faculty of Sciences, University of Porto, Rua Campo Alegre 1055, 4169-007 Porto, Portugal
    INESC TEC, Rua do Campo Alegre 1055, 4169-007 Porto, Portugal)

  • Luís Torgo

    (Faculty of Computer Science, Dalhousie University, 6050 University Av., Halifax, NS B3H 1W5, Canada)

  • Vítor Santos Costa

    (Department of Computer Science, Faculty of Sciences, University of Porto, Rua Campo Alegre 1055, 4169-007 Porto, Portugal
    INESC TEC, Rua do Campo Alegre 1055, 4169-007 Porto, Portugal)

Abstract

The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV’s bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.

Suggested Citation

  • Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:691-:d:522632
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    References listed on IDEAS

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    4. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    5. Igor Mozetič & Luis Torgo & Vitor Cerqueira & Jasmina Smailović, 2018. "How to evaluate sentiment classifiers for Twitter time-ordered data?," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-20, March.
    6. Racine, Jeff, 2000. "Consistent cross-validatory model-selection for dependent data: hv-block cross-validation," Journal of Econometrics, Elsevier, vol. 99(1), pages 39-61, November.
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    Cited by:

    1. Zhen Yu & Keming Yu & Wolfgang K. Härdle & Xueliang Zhang & Kai Wang & Maozai Tian, 2022. "Bayesian spatio‐temporal modeling for the inpatient hospital costs of alcohol‐related disorders," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 644-667, December.
    2. Olivér Hornyák & László Barna Iantovics, 2023. "AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics," Mathematics, MDPI, vol. 11(8), pages 1-24, April.

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