Abstract
Traditional autoregressive (AR) time series models have been extensively applied to predict various stationary data sets based on single point data. However, real-world system involves uncertainty due to human behaviours and incomplete information. Since the single point data is not able to represent the nature of data, fuzzy approach is necessary to deal with such uncertainties in the analysis. This paper proposes AR(1) model building based on triangular fuzzy numbers. A procedural step for building triangular fuzzy number based on standard deviation approach is provided, to handle the existence of uncertain information and the biasness during data collection. The proposed model is applied to forecast buying–selling stock market prices by using real data sets from five ASEAN countries. The results from this study show that the proposed method with triangular fuzzy numbers exhibits smaller error. That is, the proposed method is able to achieve almost similar accuracy performance as obtained by the traditional autoregressive approach, yet it also solves the uncertainties issue in the analysis.
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Acknowledgements
The author would like to extend his appreciation to the Ministry of Higher Education (MOHE) and Universiti Tun Hussein Onn Malaysia (UTHM). This research is supported by Tier 1 grant (Vot U895) and GPPS grant (Vot U975). The author thanks the anonymous viewers for their feedback. The author also would like to thank Universiti Tun Hussien Onn Malaysia for supporting this research.
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Lah, M.S.C., Arbaiy, N., Efendi, R. (2019). Stock Market Forecasting Model Based on AR(1) with Adjusted Triangular Fuzzy Number Using Standard Deviation Approach for ASEAN Countries. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_22
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DOI: https://doi.org/10.1007/978-981-13-6031-2_22
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