Hybrid heuristic model and Fuzzy C-Means for stock forecasting using Type 2 Fuzzy Time Series

Authors

  • Rineka Brylian Akbar Satriani Universitas Diponegoro
  • Farikhin Farikhin Universitas Diponegoro
  • Bayu Surarso Universitas Diponegoro

DOI:

https://doi.org/10.55324/iss.v4i1.742

Keywords:

forecast, Fuzzy C-Means, Fuzzy Time Series, heuristic, Type 2 FTS

Abstract

Forecasting is important in investment because of the inconsistent stock price pattern that requires in-depth analysis. This study proposes using a combination of heuristic and Fuzzy C-Means (FCM) models on Fuzzy Time Series Type 2. This study aims to obtain accurate forecasting results by using more data from the time series. The results show that the proposed model provides accurate forecasting. The FCM model is used to group data into clusters and form intervals. Heuristics also optimizes the performance of Fuzzy Logical Relationships Group (FLRG) by using up and down trends. Type 2 FTS is an extension of  Type 1 that uses union and intersection operators to refine fuzzy relations. The results show that the modification by combining FCM and heuristics in Type 2  FTS for stock forecasting provides excellent results with a MAPE value of 2,87%.

References

Arnita, A., Afnisah, N., & Marpaung, F. (2020). A Comparison of the Fuzzy Time Series Methods of Chen, Cheng and Markov Chain in Predicting Rainfall in Medan. Journal of Physics: Conference Series, 1462(1). https://doi.org/10.1088/1742-6596/1462/1/012044

Bai, E., Wong, W. K., Chu, W. C., Xia, M., & Pan, F. (2011). A heuristic time-invariant model for fuzzy time series forecasting. Expert Systems with Applications, 38(3). https://doi.org/10.1016/j.eswa.2010.08.059

Bajestani, N. S., & Zare, A. (2011). Forecasting TAIEX using improved type 2 fuzzy time series. Expert Systems with Applications, 38(5). https://doi.org/10.1016/j.eswa.2010.10.049

Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7

Chang, P. C., Wang, Y. W., & Liu, C. H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1). https://doi.org/10.1016/j.eswa.2005.11.021

Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3). https://doi.org/10.1016/0165-0114(95)00220-0

Huarng, K. (2001). Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems, 123(3). https://doi.org/10.1016/S0165-0114(00)00093-2

Huarng, K., & Yu, H. K. (2005). A type 2 fuzzy time series model for stock index forecasting. Physica A: Statistical Mechanics and Its Applications, 353(1–4). https://doi.org/10.1016/j.physa.2004.11.070

Li, S. T., Cheng, Y. C., & Lin, S. Y. (2008). A FCM-based deterministic forecasting model for fuzzy time series. Computers and Mathematics with Applications, 56(12). https://doi.org/10.1016/j.camwa.2008.07.033

Ningrum, R. W., Surarso, B., Farikhin, & Safarudin, Y. M. (2018). Forecasting Jakarta composite index (IHSG) based on chen fuzzy time series and firefly clustering algorithm. Journal of Physics: Conference Series, 983(1). https://doi.org/10.1088/1742-6596/983/1/012055

Pramesti, A. A., Sulandari, W., Subanti, S., & Yudhanto, Y. (2023). Forecasting the Composite Stock Price Index Using Fuzzy Time Series Type 2. Radiant, 4(2). https://doi.org/10.52187/rdt.v4i2.167

Singh, P. (2018). Rainfall and financial forecasting using fuzzy time series and neural networks based model. International Journal of Machine Learning and Cybernetics, 9(3). https://doi.org/10.1007/s13042-016-0548-5

Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series - Part I. Fuzzy Sets and Systems, 54(1). https://doi.org/10.1016/0165-0114(93)90355-L

Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series - part II. Fuzzy Sets and Systems, 62(1). https://doi.org/10.1016/0165-0114(94)90067-1

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3). https://doi.org/10.1016/S0019-9958(65)90241-X

Downloads

Published

2024-11-14

Issue

Vol. 4 No. 1 (2024): Regular Issue: October-December 2024

Section

Articles

License

Copyright (c) 2024 Rineka Brylian Akbar Satriani, Farikhin Farikhin, Bayu Surarso

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International. that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.