參考文獻 |
A. Gunasekarage and D. Power, “The profitability of moving average trading rules in South Asian stock markets,” Emerging Markets Review, vol. 2, pp. 17–33, Mar. 2001, doi: 10.1016/S1566-0141(00)00017-0.
[2] C. N. Babu and B. E. Reddy, “Selected Indian stock predictions using a hybrid ARIMA-GARCH model,” in 2014 International Conference on Advances in Electronics Computers and Communications, Oct. 2014, pp. 1–6. doi: 10.1109/ICAECC.2014.7002382.
[3] T. Li, S. Qu, and G. Huang, “Research on the Prediction of Shenzhen Growth Enterprise Market Price Index Based on EMD-ARIMA Model,” in LISS 2020, Singapore, 2021, pp. 783–795. doi: 10.1007/978-981-33-4359-7_54.
[4] A. Dingli and K. Fournier, “Financial Time Series Forecasting – A Deep Learning Approach,” International Journal of Machine Learning and Computing, vol. 7, pp. 118–122, Oct. 2017, doi: 10.18178/ijmlc.2017.7.5.632.
[5] W. Li, W. Huang, and A. Zou, “A Hybrid Deep Neural Network Model for Stock Prediction,” in 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Dec. 2021, vol. 2, pp. 483–489. doi: 10.1109/ICIBA52610.2021.9688099.
[6] X. Zhang, X. Liang, A. Zhiyuli, S. Zhang, R. Xu, and B. Wu, “AT-LSTM: An Attention-based LSTM Model for Financial Time Series Prediction,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 569, no. 5, p. 052037, Jul. 2019, doi: 10.1088/1757-899X/569/5/052037.
[7] D.-A. Ha, C.-H. Liao, K.-S. Tan, and S.-M. Yuan, “Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading,” Mathematics, vol. 9, p. 3268, Dec. 2021, doi: 10.3390/math9243268.
[8] Choi H. K., “Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model,” Aug. 2018, doi: 10.48550/arXiv.1808.01560.
[9] H. Shah, V. Bhatt, and J. Shah, “A Neoteric Technique Using ARIMA-LSTM for Time Series Analysis on Stock Market Forecasting,” in Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy, Singapore, 2022, pp. 381–392. doi: 10.1007/978-981-16-5952-2_33.
[10] Y. Li and Y. Pan, “A novel ensemble deep learning model for stock prediction based on stock prices and news,” Int J Data Sci Anal, vol. 13, no. 2, pp. 139–149, Mar. 2022, doi: 10.1007/s41060-021-00279-9.
[11] E. Mizutani and J.-S. R. Jang, “Coactive neural fuzzy modeling,” in Proceedings of ICNN’95 - International Conference on Neural Networks, Nov. 1995, vol. 2, pp. 760–765 vol.2. doi: 10.1109/ICNN.1995.487513.
[12] J. S. G.K and J. J, “MANFIS based SMART home energy management system to support SMART grid,” Peer-to-Peer Netw. Appl., vol. 13, no. 6, pp. 2177–2188, Nov. 2020, doi: 10.1007/s12083-020-00884-8.
[13] S. L. Chiu, “Fuzzy Model Identification Based on Cluster Estimation,” Journal of Intelligent & Fuzzy Systems, vol. 2, no. 3, pp. 267–278, Jan. 1994, doi: 10.3233/IFS-1994-2306.
[14] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, Nov. 1995, vol. 4, pp. 1942–1948 vol.4. doi: 10.1109/ICNN.1995.488968.
[15] S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, May 2016, doi: 10.1016/j.advengsoft.2016.01.008.
[16] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, Jun. 1965, doi: 10.1016/S0019-9958(65)90241-X.
[17] J. J. Buckley, “Fuzzy complex numbers,” Fuzzy Sets and Systems, vol. 33, no. 3, pp. 333–345, Dec. 1989, doi: 10.1016/0165-0114(89)90122-X.
[18] D. Ramot, R. Milo, M. Friedman, and A. Kandel, “Complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 171–186, Apr. 2002, doi: 10.1109/91.995119.
[19] C. Li and T.-W. Chiang, “Complex Neurofuzzy ARIMA Forecasting—A New Approach Using Complex Fuzzy Sets,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 3, pp. 567–584, Jun. 2013, doi: 10.1109/TFUZZ.2012.2226890.
[20] J.-S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665–685, May 1993, doi: 10.1109/21.256541.
[21] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, vol. 5.1, pp. 281–298, Jan. 1967.
[22] J. C. Dunn, “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters,” p. 27.
[23] K. Huarng and H.-K. Yu, “A Type 2 fuzzy time series model for stock index forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 353, pp. 445–462, Aug. 2005, doi: 10.1016/j.physa.2004.11.070.
[24] W. Zhang, S. Zhang, S. Zhang, D. Yu, and N. Huang, “A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search,” Neurocomputing, vol. 240, pp. 13–24, May 2017, doi: 10.1016/j.neucom.2017.02.054.
[25] C.-H. Cheng, G.-W. Cheng, and J.-W. Wang, “Multi-attribute fuzzy time series method based on fuzzy clustering,” Expert Systems with Applications, vol. 34, no. 2, pp. 1235–1242, Feb. 2008, doi: 10.1016/j.eswa.2006.12.013.
[26] S.-M. Chen, “Forecasting Enrollments Based on High-Order Fuzzy Time Series,” Cybernetics and Systems, vol. 33, no. 1, pp. 1–16, Jan. 2002, doi: 10.1080/019697202753306479.
[27] L.-W. Lee, L.-H. Wang, S.-M. Chen, and Y. Leu, “Handling forecasting problems based on two-factors high-order fuzzy time series,” Fuzzy Systems, IEEE Transactions on, vol. 14, pp. 468–477, Jul. 2006, doi: 10.1109/TFUZZ.2006.876367.
[28] O. Cagcag Yolcu, U. Yolcu, E. Egrioglu, and C. H. Aladag, “High order fuzzy time series forecasting method based on an intersection operation,” Applied Mathematical Modelling, vol. 40, no. 19, pp. 8750–8765, Oct. 2016, doi: 10.1016/j.apm.2016.05.012.
[29] E. Bas, U. Yolcu, E. Egrioglu, and C. H. Aladag, “A Fuzzy Time Series Forecasting Method Based on Operation of Union and Feed Forward Artificial Neural Network,” American Journal of Intelligent Systems, vol. 5, no. 3, pp. 81–91, 2015.
[30] E. Egrioglu, C. H. Aladag, U. Yolcu, V. R. Uslu, and N. A. Erilli, “Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering,” Expert Systems with Applications, vol. 38, no. 8, pp. 10355–10357, Aug. 2011, doi: 10.1016/j.eswa.2011.02.052.
[31] L. Wang, X. Liu, and W. Pedrycz, “Effective intervals determined by information granules to improve forecasting in fuzzy time series,” Expert Systems with Applications, vol. 40, no. 14, pp. 5673–5679, Oct. 2013, doi: 10.1016/j.eswa.2013.04.026.
[32] H. Guan, H. Jie, S. Guan, and A. Zhao, “A novel fuzzy-Markov forecasting model for stock fluctuation time series,” Evol. Intel., vol. 13, no. 2, pp. 133–145, Jun. 2020, doi: 10.1007/s12065-019-00328-0.
[33] H. Guo, W. Pedrycz, and X. Liu, “Fuzzy time series forecasting based on axiomatic fuzzy set theory,” Neural Comput & Applic, vol. 31, no. 8, pp. 3921–3932, Aug. 2019, doi: 10.1007/s00521-017-3325-9.
[34] R. M. Pattanayak, S. Panigrahi, and H. S. Behera, “High-Order Fuzzy Time Series Forecasting by Using Membership Values Along with Data and Support Vector Machine,” Arab J Sci Eng, vol. 45, no. 12, pp. 10311–10325, Dec. 2020, doi: 10.1007/s13369-020-04721-1.
[35] K. Bisht and A. Kumar, “A method for fuzzy time series forecasting based on interval index number and membership value using fuzzy c-means clustering,” Evol. Intel., Aug. 2021, doi: 10.1007/s12065-021-00656-0.
[36] H.-K. Yu, “A refined fuzzy time-series model for forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 346, no. 3, pp. 657–681, Feb. 2005, doi: 10.1016/j.physa.2004.07.024.
[37] Y. Wan and Y.-W. Si, “Adaptive neuro fuzzy inference system for chart pattern matching in financial time series,” Applied Soft Computing, vol. 57, pp. 1–18, Aug. 2017, doi: 10.1016/j.asoc.2017.03.023.
[38] Y. Ren, P. N. Suganthan, and N. Srikanth, “A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1793–1798, Aug. 2016, doi: 10.1109/TNNLS.2014.2351391.
[39] J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, no. 2, pp. 179–211, Apr. 1990, doi: 10.1016/0364-0213(90)90002-E.
[40] C.-H. Cheng and J.-H. Yang, “Fuzzy time-series model based on rough set rule induction for forecasting stock price,” Neurocomputing, vol. 302, pp. 33–45, Aug. 2018, doi: 10.1016/j.neucom.2018.04.014.
[41] S. Guan and A. Zhao, “A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships,” Symmetry, vol. 9, no. 10, Art. no. 10, Oct. 2017, doi: 10.3390/sym9100207.
[42] H. Guan, Z. Dai, A. Zhao, and J. He, “A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network,” PLoS One, vol. 13, no. 2, p. e0192366, 2018, doi: 10.1371/journal.pone.0192366.
[43] H. Guan, Z. Dai, S. Guan, and A. Zhao, “A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation,” Entropy, vol. 21, no. 5, Art. no. 5, May 2019, doi: 10.3390/e21050455. |