SCALABLE HUMAN KNOWLEDGE ABOUT NUMERIC TIME SERIES VARIATION AND ITS ROLE IN IMPROVING FORECASTING RESULTS
Author affiliations
DOI:
https://doi.org/10.15625/1813-9663/38/2/16125Keywords:
Linguistic Time Series, Linguistic Logical Relationship, Hedge Algebras, Quantitative Words Semantics.Abstract
Instead of handling fuzzy sets associated with linguistic (L-) labels based on the developers’ intuition immediately, the study follows the hedge algebras (HA-) approach to the time series forecasting problems, in which the linguistic time series forecasting model was, for the first time, proposed and examined in 2020. It can handle the declared forecasting L-variable word-set directly and, hence, the terminology linguistic time-series (LTS) is used instead of the fuzzy time-series (FTS). Instead of utilizing a limited number of fuzzy sets, this study views the L-variable under consideration as to the numeric forecasting variable's human linguistic counterpart. Hence, its word-domain becomes potentially infinite to positively utilize the HA-approach formalism for increasing the LTS forecasting result exactness. Because the forecasting model proposed in this study can directly handle L-words, the LTS, constructed from the numeric time series and its L-relationship groups, considered human knowledges of the given time-series variation helpful for the human-machine interface. The study shows that the proposed formalism can more easily handle the LTS forecasting models and increase their performance compared to the FTS forecasting models when the words’ number grows.
Metrics
References
Song, Q. & Chissom, B. S. Fuzzy time series and its models. Fuzzy Sets Syst. 54, 269–277 (1993). DOI: https://doi.org/10.1016/0165-0114(93)90372-O
Song, Q. & Chissom, B. S. Forecasting enrollments with fuzzy time series - part 1. Fuzzy Sets Syst. 54, 1–9 (1993). DOI: https://doi.org/10.1016/0165-0114(93)90355-L
Song, Q. & Chissom, B. S. Forecasting enrollments with fuzzy time series - part 2. Fuzzy Sets Syst. 62, 1–8 (1994). DOI: https://doi.org/10.1016/0165-0114(94)90067-1
Zadeh, L. A. Fuzzy Sets. Inf. Control 8, 338–353 (1965). DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
Chen, T.-L., Cheng, C.-H. & Teoh, H. J. Fuzzy time-series based on Fibonacci sequence for stock price forecasting. Physica A 380, 14 (2007). DOI: https://doi.org/10.1016/j.physa.2007.02.084
Chen, S. M. Forecasting enrollments based on high-order fuzzy time series. Cybern. Syst. 33, 1–16 (2002). DOI: https://doi.org/10.1080/019697202753306479
Aladag, C. H., Yolcu, U. & Egrioglu, E. A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks. Math. Comput. Simul. 81, 875–882 (2010). DOI: https://doi.org/10.1016/j.matcom.2010.09.011
Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R. & Basaran, M. A. Finding an optimal interval length in high order fuzzy time series. Expert Syst. Appl. 37, 5052–5055 (2010). DOI: https://doi.org/10.1016/j.eswa.2009.12.006
Bose, M. & Mali, K. A novel data partitioning and rule selection technique for modeling high-order fuzzy time series. Appl. Soft Comput. 63, 87–96 (2018). DOI: https://doi.org/10.1016/j.asoc.2017.11.011
Chen, S. M. & Chen, C. D. TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans. Fuzzy Syst. 19, 1–12 (2011). DOI: https://doi.org/10.1109/TFUZZ.2010.2073712
Singh, P. & Borah, B. Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization. Int. J. Approx. Reason. 55, 812–833 (2014). DOI: https://doi.org/10.1016/j.ijar.2013.09.014
Chen, S. M. & Phuong, B. D. H. Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Syst. 118, 204–216 (2017). DOI: https://doi.org/10.1016/j.knosys.2016.11.019
Zhang, W., Zhang, S., Zhang, S., Yu, D. & Huang, N. N. A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search. Neurocomputing 240, 13–24 (2017). DOI: https://doi.org/10.1016/j.neucom.2017.02.054
Singh, A., Gautam, S. S. & Singh, S. R. A New Type 2 Fuzzy Time Series Forecasting Model Based on Three-Factors Fuzzy Logical Relationships. Int. J. Uncertainty, Fuzziness Knowledge-Based Syst. (2019) doi:10.1142/s0218488519500120. DOI: https://doi.org/10.1142/S0218488519500120
Huarng, K. & Yu, H.-K. A Type 2 fuzzy time series model for stock index forecasting. Phys. A Stat. Mech. its Appl. 353, 445–462 (2005). DOI: https://doi.org/10.1016/j.physa.2004.11.070
Bisht, K. & Kumar, S. Intuitionistic Fuzzy Set-Based Computational Method for Financial Time Series Forecasting. Fuzzy Inf. Eng. 10, 307–323 (2018). DOI: https://doi.org/10.1080/16168658.2019.1631557
Cagcag Yolcu, O., Bas, E., Egrioglu, E. & Yolcu, U. A new intuitionistic fuzzy functions approach based on hesitation margin for time-series prediction. Soft Comput. 2, (2019). DOI: https://doi.org/10.1007/s00500-019-04432-2
Chen, L.-S., Chen, M.-Y., Chang, J.-R. & Yu, P.-Y. An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method. Int. J. Comput. Intell. Syst. 14, 550–559 (2021). DOI: https://doi.org/10.2991/ijcis.d.210106.002
Singh, S. & Abhishekh, G. A modified weighted method of time series forecasting in intuitionistic fuzzy environment. OPSEARCH (2020) doi:10.1007/s12597-020-00455-8. DOI: https://doi.org/10.1007/s12597-020-00455-8
Bas, E., Yolcu, U. & Egrioglu, E. Intuitionistic fuzzy time series functions approach for time series forecasting. Granul. Comput. (2020) doi:10.1007/s41066-020-00220-8. DOI: https://doi.org/10.1007/s41066-020-00220-8
Egrioglu, E., Bas, E. & Yolcu, U. Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony. Granul. Comput. (2018). DOI: https://doi.org/10.1007/s41066-018-00143-5
Egrioglu, E., Bas, E., Yolcu, U. & Yen, M. Picture fuzzy time series : Defining , modeling and creating a new forecasting method. Eng. Appl. Artif. Intell. 88, 13 (2020). DOI: https://doi.org/10.1016/j.engappai.2019.103367
Huarng, K. Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. 123, 387–394 (2001). DOI: https://doi.org/10.1016/S0165-0114(00)00057-9
Huarng, K. & Yu, T. H. K. Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans. Syst. Man, Cybern. Part B Cybern. 36, 328–340 (2006). DOI: https://doi.org/10.1109/TSMCB.2005.857093
Chen, S. M., Wang, N. Y. & Pan, J. S. Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships. Expert Syst. Appl. 36, 11070–11076 (2009). DOI: https://doi.org/10.1016/j.eswa.2009.02.085
Wang, N. Y. & Chen, S. M. Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series. Expert Syst. Appl. 36, 2143–2154 (2009). DOI: https://doi.org/10.1016/j.eswa.2007.12.013
Khashei, M., Reza Hejazi, S., Bijari, M., Hejazi, R. & Bijari, M. A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets Syst. 159, 769–786 (2007). DOI: https://doi.org/10.1016/j.fss.2007.10.011
Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R. & Basaran, M. A. A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Syst. Appl. 36, 10589–10594 (2009). DOI: https://doi.org/10.1016/j.eswa.2009.02.057
Egrioglu, E., Fildes, R. & Baş, E. Recurrent fuzzy time series functions approaches for forecasting. Granul. Comput. 1–8 (2021) doi:10.1007/s41066-021-00257-3. DOI: https://doi.org/10.1007/s41066-021-00257-3
Alpaslan, F., Eğrioğlu, E., Hakan Aladağ, Ç. & Tiring, E. An Statistical Research on Feed Forward Neural Networks for Forecasting Time Series. Am. J. Intell. Syst. 2, 21–25 (2012). DOI: https://doi.org/10.5923/j.ajis.20120203.02
Singh, P. & Huang, Y.-P. A High-Order Neutrosophic-Neuro-Gradient Descent Algorithm-Based Expert System for Time Series Forecasting. Int. J. Fuzzy Syst. (2019) doi:10.1007/s40815-019-00690-2. DOI: https://doi.org/10.1007/s40815-019-00690-2
Cagcag Yolcu, O. & Alpaslan, F. Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process. Appl. Soft Comput. 66, 18–33 (2018). DOI: https://doi.org/10.1016/j.asoc.2018.02.007
Kuremoto, T., Hirata, T., Obayashi, M., Mabu, S. & Kobayashi, K. Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting. in Time Series Analysis - Data, Methods, and Applications 18 (2019). doi:http://dx.doi.org/10.5772/57353. DOI: https://doi.org/10.5772/intechopen.85457
Panigrahi, S. & Behera, H. S. A study on leading machine learning techniques for high order fuzzy time series forecasting. Eng. Appl. Artif. Intell. 87, 103245 (2020). DOI: https://doi.org/10.1016/j.engappai.2019.103245
Chen, S. M. & Hwang, J. R. Temperature Prediction Using Fuzzy Time Series. IEEE Trans. Syst. Man, Cybern. Part B Cybern. 30, 263–275 (2000). DOI: https://doi.org/10.1109/3477.836375
Lee, L.-W., Wang, L.-H. & Chen, S.-M. Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Syst. Appl. 33, 12 (2007). DOI: https://doi.org/10.1016/j.eswa.2006.05.015
Hsu, L.-Y. et al. Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Syst. Appl. 37, 15 (2010). DOI: https://doi.org/10.1016/j.eswa.2009.09.015
Wang, C. H. & Hsu, L. C. Constructing and applying an improved fuzzy time series model: Taking the tourism industry for example. Expert Syst. Appl. 34, 2732–2738 (2008). DOI: https://doi.org/10.1016/j.eswa.2007.05.042
Huarng, K.-H., Yu, T. H.-K., Moutinho, L. & Wang, Y.-C. Forecasting tourism demand by fuzzy time series models. Int. J. Cult. Tour. Hosp. Res. 6, 12 (2012). DOI: https://doi.org/10.1108/17506181211265095
Tsaur, R. C. & Kuo, T. C. The adaptive fuzzy time series model with an application to Taiwan’s tourism demand. Expert Syst. Appl. 38, 9164–9171 (2011). DOI: https://doi.org/10.1016/j.eswa.2011.01.059
Yu, H. K. Weighted fuzzy time series models for TAIEX forecasting. Phys. A Stat. Mech. its Appl. 349, 609–624 (2005). DOI: https://doi.org/10.1016/j.physa.2004.11.006
Qiu, W., Liu, X. & Li, H. A generalized method for forecasting based on fuzzy time series. Expert Syst. Appl. 38, 10446–10453 (2011). DOI: https://doi.org/10.1016/j.eswa.2011.02.096
Duru, O. A fuzzy integrated logical forecasting model for dry bulk shipping index forecasting: An improved fuzzy time series approach. Expert Syst. Appl. 37, 5372–5380 (2010). DOI: https://doi.org/10.1016/j.eswa.2010.01.019
Lee, M. H., Efendi, R. & Ismail, Z. Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series. MATEMATIKA 25, 12 (2009).
Cheng, C.-H., Teoh, H. J., Chiang, C.-H. & Chen, T.-L. Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Syst. Appl. 34, 8 (2008). DOI: https://doi.org/10.1016/j.eswa.2006.12.021
Efendi, R., Ismail, Z., Sarmin, N. H. & Mat Deris, M. A reversal model of fuzzy time series in regional load forecasting. Int. J. Energy Stat. 03, 1550003 (2015). DOI: https://doi.org/10.1142/S2335680415500039
Rubio, A., Bermúdez, J. D. & Vercher, E. Forecasting portfolio returns using weighted fuzzy time series methods. Int. J. Approx. Reason. 75, 1–12 (2016). DOI: https://doi.org/10.1016/j.ijar.2016.03.007
Ortiz-Arroyo, D. & Poulsen, J. R. A Weighted Fuzzy Time Series Forecasting Model. Indian J. Sci. Technol. 11, 1–11 (2018). DOI: https://doi.org/10.17485/ijst/2018/v11i27/130708
Ho, N. C. & Wechler, W. Hedge Algebras: An algebraic approach to structure of sets of linguistic truth values. Fuzzy Sets Syst. 35, 281–293 (1990). DOI: https://doi.org/10.1016/0165-0114(90)90002-N
Ho, N. C. & Wechler, W. Extended hedge algebras and their application to fuzzy logic. Fuzzy Sets Syst. 52, 259–281 (1992). DOI: https://doi.org/10.1016/0165-0114(92)90237-X
Ho, N. C., Lan, V. N. & Viet, L. X. Optimal hedge-algebras-based controller: Design and application. Fuzzy Sets Syst. 159, 32 (2008). DOI: https://doi.org/10.1016/j.fss.2007.11.001
Bui, H. Le, Le, T. A. & Bui, V. B. Explicit formula of hedge-algebras-based fuzzy controller and applications in structural vibration control. Appl. Soft Comput. 60, 150–166 (2017). DOI: https://doi.org/10.1016/j.asoc.2017.06.045
Tran, D. T., Bui, V. B., Le, T. A. & Bui, H. Le. Vibration control of a structure using sliding-mode hedge-algebras-based controller. Soft Comput. 23, 2047–2059 (2017). DOI: https://doi.org/10.1007/s00500-017-2919-6
Vukadinovic, D., Basic, M., Nguyen, C. H., Vu, N. L. & Nguyen, T. D. Hedge-algebra-based voltage controller for a self-excited induction generator. Control Eng. Pract. 30, 78–90 (2014). DOI: https://doi.org/10.1016/j.conengprac.2014.05.006
Nguyen, C. H., Tran, T. S. & Pham, D. P. Modeling of a semantics core of linguistic terms based on an extension of hedge algebra semantics and its application. Knowledge-Based Syst. 67, 19 (2014). DOI: https://doi.org/10.1016/j.knosys.2014.04.047
Nguyen, C. H., Hoang, V. T. & Nguyen, V. L. A discussion on interpretability of linguistic rule based systems and its application to solve regression problems. Knowledge-Based Syst. 88, 107–133 (2015). DOI: https://doi.org/10.1016/j.knosys.2015.08.002
Ngo, H. H., Nguyen, C. H. & Nguyen, V. Q. Multichannel image contrast enhancement based on linguistic rule-based intensificators. Appl. Soft Comput. 76, 744–763 (2019). DOI: https://doi.org/10.1016/j.asoc.2018.12.034
Hieu, N. D., Ho, N. C. & Lan, V. N. Enrollment Forecasting Based on Linguistic Time Series. J. Comput. Sci. Cybern. 36, 119–137 (2020). DOI: https://doi.org/10.15625/1813-9663/36/2/14396
Hieu, N. D., Ho, N. C. & Lan, V. N. An efficient fuzzy time series forecasting model based on quantifying semantics of words. in RIVF International Conference (2020). DOI: https://doi.org/10.1109/RIVF48685.2020.9140755
Chen, S. M. Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81, 311–319 (1996). DOI: https://doi.org/10.1016/0165-0114(95)00220-0
Ho, N. C. & Long, N. Van. Fuzziness measure on complete hedge algebras and quantifying semantics of terms in linear hedge algebras. Fuzzy Sets Syst. 158, 452–471 (2007). DOI: https://doi.org/10.1016/j.fss.2006.10.023
N. C. Ho, P. T. Lan, N. N. Tu, H. C. Ha, N. T. Anh, "The linguistic summarization and the interpretability, scalability of fuzzy representations of multilevel semantic structures of word-domains", Microprocessors and Microsystems, vol. 81, 103641, 2021. DOI: https://doi.org/10.1016/j.micpro.2020.103641
Downloads
Published
How to Cite
Issue
Section
License
1. We hereby assign copyright of our article (the Work) in all forms of media, whether now known or hereafter developed, to the Journal of Computer Science and Cybernetics. We understand that the Journal of Computer Science and Cybernetics will act on my/our behalf to publish, reproduce, distribute and transmit the Work.2. This assignment of copyright to the Journal of Computer Science and Cybernetics is done so on the understanding that permission from the Journal of Computer Science and Cybernetics is not required for me/us to reproduce, republish or distribute copies of the Work in whole or in part. We will ensure that all such copies carry a notice of copyright ownership and reference to the original journal publication.
3. We warrant that the Work is our results and has not been published before in its current or a substantially similar form and is not under consideration for another publication, does not contain any unlawful statements and does not infringe any existing copyright.
4. We also warrant that We have obtained the necessary permission from the copyright holder/s to reproduce in the article any materials including tables, diagrams or photographs not owned by me/us.
