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New Techniques to Perform Cross-Validation for Time Series Models

Author

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  • A. Vamsikrishna

    (Symbiosis Statistical Institute, Symbiosis International (Deemed University))

  • E. V. Gijo

    (Indian Statistical Institute)

Abstract

Model validation for time series models has always been a challenge due to a lot of complexities. The presence of auto-correlation in the data creates a challenge to the conventional cross validation techniques like k-fold cross validation to be implemented for time-series models. In this paper, two weighted k-fold time series split cross-validation techniques are proposed for this purpose. The proposed techniques were validated using the opening price data of cryptocurrency. Mean squared error (MSE), Mean absolute error (MAE) and Mean absolute percentage error (MAPE) were the selected metrics to validate the proposed techniques. Both the techniques were found to give robust results; however, the Exponential weighted K-fold time series split cross validation (EWKCV) technique was seen to perform better than Generally weighted K-fold time series split cross validation (GWKCV) technique. The results of the proposed techniques, along with the results of simple train-test split for the time-series models, is seen to give better result.

Suggested Citation

  • A. Vamsikrishna & E. V. Gijo, 2024. "New Techniques to Perform Cross-Validation for Time Series Models," SN Operations Research Forum, Springer, vol. 5(2), pages 1-12, June.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00334-8
    DOI: 10.1007/s43069-024-00334-8
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    References listed on IDEAS

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    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Cia Vei Tan & Sarbhan Singh & Chee Herng Lai & Ahmed Syahmi Syafiq Md Zamri & Sarat Chandra Dass & Tahir Bin Aris & Hishamshah Mohd Ibrahim & Balvinder Singh Gill, 2022. "Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia," IJERPH, MDPI, vol. 19(3), pages 1-12, January.
    3. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    4. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
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