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A comparison of ARIMA forecasting and heuristic modelling

Author

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  • Chi-Chen Wang
  • Yun-Sheng Hsu
  • Cheng-Hwai Liou

Abstract

The study compares the application of the forecasting methods Autoregressive Integrated Moving Average (ARIMA) time series model and fuzzy time series by heuristic models on the amount of Taiwan export. When our model prolongs the sample period, the predicted error is smaller for the ARIMA model than for the heuristic model. Moreover, the predicted trajectory of the ARIMA model is much closer to the realistic trend than the heuristic model. Thus, the ARIMA model can forecast the export amount more accurately than the heuristic models. In the economic viewpoints, the amount of Taiwan exports is mainly attributable to external factors. In addition, the impact reduces with time and the export with lags 12 or 13 do not affect current export amount anymore. If the sample period is shorter, the heuristic models outperform ARIMA models. A heuristic fuzzy time series model can be utilized to predict export values accurately, when only small set of data is available.

Suggested Citation

  • Chi-Chen Wang & Yun-Sheng Hsu & Cheng-Hwai Liou, 2011. "A comparison of ARIMA forecasting and heuristic modelling," Applied Financial Economics, Taylor & Francis Journals, vol. 21(15), pages 1095-1102.
    • Handle: RePEc:taf:apfiec:v:21:y:2011:i:15:p:1095-1102
    DOI: 10.1080/09603107.2010.537629
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    Citations

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    Cited by:

    1. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    2. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    3. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    4. Colubi, Ana & Ramos-Guajardo, Ana Belén, 2023. "Fuzzy sets and (fuzzy) random sets in Econometrics and Statistics," Econometrics and Statistics, Elsevier, vol. 26(C), pages 84-98.

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