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題名 以相空間分割與階層序列合成預測學生表現
Predicting Student Performance with Phase Space Partition and Hierarchical Sequence Synthesis
作者 許甄珉
Hsu, Chen-Min
貢獻者 郁方
Yu, Fang
許甄珉
Hsu, Chen-Min
關鍵詞 相空間
預測
階層序列
合成
GHSOM
Phase Space Partition
Hierarchical Sequence
日期 2020
上傳時間 2-Sep-2020 11:46:23 (UTC+8)
摘要 由於科技發展迅速，許多學校與教師皆使用e-learning平台作為教學輔助的工具，因此平台記錄了學生大量的學習行為，而如何充分利用這些數據來提高課程的有效性，是大多數學者關注的議題。我們希望利用平台的大量紀錄資料，透過機器學習方法來預測學生的學習行為，滿足不同學生的個別需求，也可對具有風險的學生採取補救的措施，讓e-learning平台從原本只提供數據的被動角色轉換為具有評估學習狀態能力的主動角色，從學生學習的進程預測未來的學習狀態與表現，進而提供教師額外的資訊，協助改善學生學習狀態。
Due to the rapid development of science and technology, many schools and teachers use the e-learning platform as a teaching aid tool. Therefore, the platform records a large number of students` learning behaviors. How to make full use of these data to improve the effectiveness of the curriculum is what most scholars are concerned about. We hope to use a large amount of recorded data on the platform to predict students` learning behaviors through machine learning methods to meet the individual needs of different students. We can also take remedial measures for students at risk, with the ability to assess the learning status, predicting the future learning status and performance from the student`s learning process, and then providing teachers with additional information to help improve the student`s learning status. So we can turn the e-learning platform from playing a passive role on data-access-record to an active one with evaluation-caution-transformation. There are three major challenges in predicting student performance. The first is that student records are often high-dimensional data, which makes the prediction effect poor. The second problem is that the data is time-series data, so the order of the data must be considered. In order, the third is that in most cases, we only have partial information, so it is challenging to use partial data to make accurate predictions. In this paper, we will use phase space partition to split high-dimensional data, and use the symbolic label to represent partitions. These symbolic label sequences can be regarded as discrete sequences. Finally, HiSeqGAN, the neural network of sequence synthesis is used to generate a large amount of data, and use Label Error to calculate the symbol label distance between the generated data and the real data to predict the future performance of students. Then use the methods mentioned above to solve the three major challenges.
參考文獻 [1] A., S., Vinodhini, G., and Chandrasekaran, R. M. (2018). Predicting students’ academic performance in the university using meta decision tree classifiers. J. Comput.
Sci., 14(5):654–662.
[2] Arnold, K. E. and Pistilli, M. D. (2012). Course signals at purdue: using learning analytics to increase student success. In Dawson, S., Haythornthwaite, C., Shum, S. B.,Gasevic, D., and Ferguson, R., editors, Second International Conference on Learning Analytics and Knowledge, LAK 2012, Vancouver, BC, Canada, April 29 - May 02, 2012, pages 267–270. ACM.
[3] Asif, R., Merceron, A., Ali, S. A., and Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Comput. Educ., 113:177–194.
[4] Baradwaj, B. and Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2:63–69.
[5] Coulom, R. (2006). Efficient selectivity and backup operators in monte-carlo tree search. In van den Herik, H. J., Ciancarini, P., and Donkers, H. H. L. M., editors,
Computers and Games, 5th International Conference, CG 2006, Turin, Italy, May 29-31, 2006. Revised Papers, volume 4630 of Lecture Notes in Computer Science, pages 72–83. Springer.
[6] Dittenbach, M., Merkl, D., and Rauber, A. (2000). The growing hierarchical self-organizing map. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and
Perspectives for the New Millennium, volume 6, pages 15–19. IEEE.
[7] Esteban, C., Hyland, S., and Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans.
[8] Fairos, W., Wan Yaacob, W. F., Azlin, S., Nasir, S., Faizah, W., Sobri, N., Mara, C., and Kelantan, M. (2019). Supervised data mining approach for predicting student
performance. Indonesian Journal of Electrical Engineering and Computer Science, 16:1584–1592.
[9] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in
neural information processing systems, pages 2672–2680.
[10] Grayson, A., Miller, H., and Clarke, D. D. (1998). Identifying barriers to help-seeking: a qualitative analysis of students’ preparedness to seek help from tutors. British Journal of Guidance & Counselling, 26(2):237–253.
[11] Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., and Wang, J. (2018). Long text generation via adversarial training with leaked information.
[12] Hadamard (1898). Les surfaces "a courbures opposées et leurs lignes géodésiques. Journal de Mathématiques Pures et Appliquées, 4:27–74.
[13] Hadriche, A., Jmail, N., and Elleuch, R. (2014). Different methods of partitioning the phase space of a dynamic system. International Journal of Computer Applications,93:1–5.
[14] Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
[15] Hu, Y.-H., Lo, C.-l., and Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior.
[16] Kennel, M. and Buhl, M. (2003). Estimating good discrete partitions from observed data: Symbolic false nearest neighbors. Physical review letters, 91:084102.
[17] Khatkhate, A. (2018). Anomaly detection in electromechanical systems using symbolic dynamics.
[18] Kim, B., Vizitei, E., and Ganapathi, V. (2018). Gritnet: Student performance prediction with deep learning. In Boyer, K. E. and Yudelson, M., editors, Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018, Buffalo, NY, USA, July 15-18, 2018. International Educational Data Mining Society (IEDMS).
[19] Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480.
[20] Kumar, A., Selvam, R., and Kumar, K. (2018). Review on prediction algorithms in educational data mining. International Journal of Pure and Applied Mathematics,
118:531–536.
[21] Lin, T. Y., Chuang, H. H. C., and Yu, F. (2018). Tracking supply chain process variability with unsupervised cluster traversal. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and
Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pages 966–973. IEEE.
[22] Luo, J., Sorour, S. E., Mine, T., and Goda, K. (2015). Predicting student grade based on free-style comments using word2vec and ANN by considering prediction results obtained in consecutive lessons. In Santos, O. C., Boticario, J., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J. M., Mihaescu, M. C., Moreno, P., Hershkovitz, A., Ventura, S., and Desmarais, M. C., editors, Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29, 2015, pages 396–399. International Educational Data Mining Society (IEDMS).
[23] Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., and Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. J. Assoc. Inf. Sci. Technol., 60(2):372–380.
[24] Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. In 5-th Berkeley Symposium on Mathematical Statistics and Probability, pages 281–297.
[25] Okubo, F., Yamashita, T., Shimada, A., and Ogata, H. (2017). A neural network approach for students’ performance prediction. pages 598–599.
[26] Rajagopalan, V., Ray, A., Samsi, R., and Mayer, J. (2007). Pattern identification in dynamical systems via symbolic time series analysis. Pattern Recognition, 40:2897–
2907.
[27] Rattadilok, P. and Roadknight, C. (2018). Improving student’s engagement through the use of learning modules, instantaneous feedback and automated marking. In 2018
IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pages 802–806.
[28] Rendle, S. (2010). Factorization machines. In Webb, G. I., Liu, B., Zhang, C., Gunopulos, D., and Wu, X., editors, The 10th IEEE International Conference on Data Mining, pages 995–1000. IEEE Computer Society.
[29] Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014). Deterministic policy gradient algorithms. 31st International Conference on Machine
Learning, ICML 2014, 1.
[30] Sloane, N. and Wyner, A. (2009). Prediction and entropy of printed english. pages 194–208.
[31] Soni, A., Kumar, V., Kaur, R., and Hemavathi, D. (2018). Predicting student performance using data mining techniques. International Journal of Pure and Applied
Mathematics, 119:221–226.
[32] Subbu, A. and Ray, A. (2008). Space partitioning via hilbert transform for symbolic time series analysis. Applied Physics Letters, 92:084107–084107.
[33] Sweeney, M., Lester, J., and Rangwala, H. (2015). Next-term student grade prediction. In 2015 IEEE International Conference on Big Data, Big Data 2015, Santa Clara,
CA, USA, October 29 - November 1, 2015, pages 970–975. IEEE Computer Society.
[34] Tien, Y., Hsu, C., and Yu, F. (2019). Hiseqgan: Hierarchical sequence synthesis and prediction. In Tetko, I. V., Kurková, V., Karpov, P., and Theis, F. J., editors,
Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II, volume 11728 of Lecture Notes in Computer Science, pages 621–638. Springer.
[35] Vega-Márquez, B., Rubio-Escudero, C., Riquelme, J. C., and Nepomuceno-Chamorro, I. A. (2019). Creation of synthetic data with conditional generative adversarial networks. In 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) - Seville, Spain, May 13-15, 2019, Proceedings, volume 950 of Advances in Intelligent Systems and Computing, pages 231–240. Springer.
[36] Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn., 8(3–4):229–256.
[37] Williams, R. J. and Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270–280.
[38] Yu, L., Zhang, W., Wang, J., and Yu, Y. (2017). Seqgan: Sequence generative adversarial nets with policy gradient. In Singh, S. P. and Markovitch, S., editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pages 2852–2858. AAAI Press.
描述 碩士
國立政治大學
資訊管理學系
107356019
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107356019
資料類型 thesis
dc.contributor.advisor 郁方zh_TW
dc.contributor.advisor Yu, Fangen_US
dc.contributor.author (Authors) 許甄珉zh_TW
dc.contributor.author (Authors) Hsu, Chen-Minen_US
dc.creator (作者) 許甄珉zh_TW
dc.creator (作者) Hsu, Chen-Minen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Sep-2020 11:46:23 (UTC+8)-
dc.date.available 2-Sep-2020 11:46:23 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2020 11:46:23 (UTC+8)-
dc.identifier (Other Identifiers) G0107356019en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131494-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 107356019zh_TW
dc.description.abstract (摘要) 由於科技發展迅速，許多學校與教師皆使用e-learning平台作為教學輔助的工具，因此平台記錄了學生大量的學習行為，而如何充分利用這些數據來提高課程的有效性，是大多數學者關注的議題。我們希望利用平台的大量紀錄資料，透過機器學習方法來預測學生的學習行為，滿足不同學生的個別需求，也可對具有風險的學生採取補救的措施，讓e-learning平台從原本只提供數據的被動角色轉換為具有評估學習狀態能力的主動角色，從學生學習的進程預測未來的學習狀態與表現，進而提供教師額外的資訊，協助改善學生學習狀態。zh_TW
dc.description.abstract (摘要) Due to the rapid development of science and technology, many schools and teachers use the e-learning platform as a teaching aid tool. Therefore, the platform records a large number of students` learning behaviors. How to make full use of these data to improve the effectiveness of the curriculum is what most scholars are concerned about. We hope to use a large amount of recorded data on the platform to predict students` learning behaviors through machine learning methods to meet the individual needs of different students. We can also take remedial measures for students at risk, with the ability to assess the learning status, predicting the future learning status and performance from the student`s learning process, and then providing teachers with additional information to help improve the student`s learning status. So we can turn the e-learning platform from playing a passive role on data-access-record to an active one with evaluation-caution-transformation. There are three major challenges in predicting student performance. The first is that student records are often high-dimensional data, which makes the prediction effect poor. The second problem is that the data is time-series data, so the order of the data must be considered. In order, the third is that in most cases, we only have partial information, so it is challenging to use partial data to make accurate predictions. In this paper, we will use phase space partition to split high-dimensional data, and use the symbolic label to represent partitions. These symbolic label sequences can be regarded as discrete sequences. Finally, HiSeqGAN, the neural network of sequence synthesis is used to generate a large amount of data, and use Label Error to calculate the symbol label distance between the generated data and the real data to predict the future performance of students. Then use the methods mentioned above to solve the three major challenges.en_US
dc.description.tableofcontents 1 Introduction . . . . . . . . . . 1
2 Related Work . . . . . . . . . . 4
2.1 Intelligent e-learning systems. . . . . . . . . . 4
2.2 Phase Space Partition . . . . . . . . . . 5
2.3 Discrete Sequence Synthesis . . . . . . . . . . 6
3 Methodology 8
3.1 Overview . . . . . . . . . . 8
3.2 Phase Space Partition . . . . . . . . . . 8
3.2.1 Growing Hierarchical Self-Organizing Map (GHSOM) . . . . . . . . . . 9
3.2.2 Symbolic Labels with Hierarchy . . . . . . . . . . 10
3.2.3 Label Error with Symbolic Labels. . . . . . . . . . 11
3.2.4 Label Sequence Error. . . . . . . . . . 12
3.3 Discrete sequence synthesis: HiSeqGAN . . . . . . . . . . 13
3.3.1 HiSeqGAN Overview . . . . . . . . . . 13
3.3.2 Discrete Sequence Synthesis . . . . . . . . . . 14
3.4 Sequence Prediction . . . . . . . . . . 16
4 Experiments . . . . . . . . . . 18
4.1 Logs of e-learning Platform . . . . . . . . . . 18
4.2 Predict Student Semester base on Midterm and Final Grade . . . . . . . . . . 21
4.3 Phase Space Partition on Student Weekly States . . . . . . . . . . 22
4.3.1 Effects of τ1 and τ 2 on GHSOM clustering . . . . . . . . . . 22
4.3.2 Comparison of partitioning methods. . . . . . . . . . 23
4.4 Symbolic Sequence on State Partitions . . . . . . . . . . 24
4.5 Student Partitions with Symbolic Label . . . . . . . . . . 25
4.6 State Partition Sequence Synthesis . . . . . . . . . . 25
4.6.1 Improve RNN Prediction with Synthesis Sequence . . . . . . . . . . 27 
4.7 Use Partial Sequence to Predict Student Partition . . . . . . . . . . . . . . 27 
4.8 Improve Accuracy in Predicting Semester Grades . . . . . . . . . . . . . . 31 
5 Conclusion . . . . . . . . . . 35 
6 Reference . . . . . . . . . . 36		zh_TW
dc.format.extent 4949903 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107356019en_US
dc.subject (關鍵詞) 相空間zh_TW
dc.subject (關鍵詞) 預測zh_TW
dc.subject (關鍵詞) 階層序列zh_TW
dc.subject (關鍵詞) 合成zh_TW
dc.subject (關鍵詞) GHSOMen_US
dc.subject (關鍵詞) Phase Space Partitionen_US
dc.subject (關鍵詞) Hierarchical Sequenceen_US
dc.title (題名) 以相空間分割與階層序列合成預測學生表現zh_TW
dc.title (題名) Predicting Student Performance with Phase Space Partition and Hierarchical Sequence Synthesisen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] A., S., Vinodhini, G., and Chandrasekaran, R. M. (2018). Predicting students’ academic performance in the university using meta decision tree classifiers. J. Comput.
Sci., 14(5):654–662.
[2] Arnold, K. E. and Pistilli, M. D. (2012). Course signals at purdue: using learning analytics to increase student success. In Dawson, S., Haythornthwaite, C., Shum, S. B.,Gasevic, D., and Ferguson, R., editors, Second International Conference on Learning Analytics and Knowledge, LAK 2012, Vancouver, BC, Canada, April 29 - May 02, 2012, pages 267–270. ACM.
[3] Asif, R., Merceron, A., Ali, S. A., and Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Comput. Educ., 113:177–194.
[4] Baradwaj, B. and Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2:63–69.
[5] Coulom, R. (2006). Efficient selectivity and backup operators in monte-carlo tree search. In van den Herik, H. J., Ciancarini, P., and Donkers, H. H. L. M., editors,
Computers and Games, 5th International Conference, CG 2006, Turin, Italy, May 29-31, 2006. Revised Papers, volume 4630 of Lecture Notes in Computer Science, pages 72–83. Springer.
[6] Dittenbach, M., Merkl, D., and Rauber, A. (2000). The growing hierarchical self-organizing map. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and
Perspectives for the New Millennium, volume 6, pages 15–19. IEEE.
[7] Esteban, C., Hyland, S., and Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans.
[8] Fairos, W., Wan Yaacob, W. F., Azlin, S., Nasir, S., Faizah, W., Sobri, N., Mara, C., and Kelantan, M. (2019). Supervised data mining approach for predicting student
performance. Indonesian Journal of Electrical Engineering and Computer Science, 16:1584–1592.
[9] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in
neural information processing systems, pages 2672–2680.
[10] Grayson, A., Miller, H., and Clarke, D. D. (1998). Identifying barriers to help-seeking: a qualitative analysis of students’ preparedness to seek help from tutors. British Journal of Guidance & Counselling, 26(2):237–253.
[11] Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., and Wang, J. (2018). Long text generation via adversarial training with leaked information.
[12] Hadamard (1898). Les surfaces "a courbures opposées et leurs lignes géodésiques. Journal de Mathématiques Pures et Appliquées, 4:27–74.
[13] Hadriche, A., Jmail, N., and Elleuch, R. (2014). Different methods of partitioning the phase space of a dynamic system. International Journal of Computer Applications,93:1–5.
[14] Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
[15] Hu, Y.-H., Lo, C.-l., and Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior.
[16] Kennel, M. and Buhl, M. (2003). Estimating good discrete partitions from observed data: Symbolic false nearest neighbors. Physical review letters, 91:084102.
[17] Khatkhate, A. (2018). Anomaly detection in electromechanical systems using symbolic dynamics.
[18] Kim, B., Vizitei, E., and Ganapathi, V. (2018). Gritnet: Student performance prediction with deep learning. In Boyer, K. E. and Yudelson, M., editors, Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018, Buffalo, NY, USA, July 15-18, 2018. International Educational Data Mining Society (IEDMS).
[19] Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480.
[20] Kumar, A., Selvam, R., and Kumar, K. (2018). Review on prediction algorithms in educational data mining. International Journal of Pure and Applied Mathematics,
118:531–536.
[21] Lin, T. Y., Chuang, H. H. C., and Yu, F. (2018). Tracking supply chain process variability with unsupervised cluster traversal. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and
Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pages 966–973. IEEE.
[22] Luo, J., Sorour, S. E., Mine, T., and Goda, K. (2015). Predicting student grade based on free-style comments using word2vec and ANN by considering prediction results obtained in consecutive lessons. In Santos, O. C., Boticario, J., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J. M., Mihaescu, M. C., Moreno, P., Hershkovitz, A., Ventura, S., and Desmarais, M. C., editors, Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29, 2015, pages 396–399. International Educational Data Mining Society (IEDMS).
[23] Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., and Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. J. Assoc. Inf. Sci. Technol., 60(2):372–380.
[24] Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. In 5-th Berkeley Symposium on Mathematical Statistics and Probability, pages 281–297.
[25] Okubo, F., Yamashita, T., Shimada, A., and Ogata, H. (2017). A neural network approach for students’ performance prediction. pages 598–599.
[26] Rajagopalan, V., Ray, A., Samsi, R., and Mayer, J. (2007). Pattern identification in dynamical systems via symbolic time series analysis. Pattern Recognition, 40:2897–
2907.
[27] Rattadilok, P. and Roadknight, C. (2018). Improving student’s engagement through the use of learning modules, instantaneous feedback and automated marking. In 2018
IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pages 802–806.
[28] Rendle, S. (2010). Factorization machines. In Webb, G. I., Liu, B., Zhang, C., Gunopulos, D., and Wu, X., editors, The 10th IEEE International Conference on Data Mining, pages 995–1000. IEEE Computer Society.
[29] Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014). Deterministic policy gradient algorithms. 31st International Conference on Machine
Learning, ICML 2014, 1.
[30] Sloane, N. and Wyner, A. (2009). Prediction and entropy of printed english. pages 194–208.
[31] Soni, A., Kumar, V., Kaur, R., and Hemavathi, D. (2018). Predicting student performance using data mining techniques. International Journal of Pure and Applied
Mathematics, 119:221–226.
[32] Subbu, A. and Ray, A. (2008). Space partitioning via hilbert transform for symbolic time series analysis. Applied Physics Letters, 92:084107–084107.
[33] Sweeney, M., Lester, J., and Rangwala, H. (2015). Next-term student grade prediction. In 2015 IEEE International Conference on Big Data, Big Data 2015, Santa Clara,
CA, USA, October 29 - November 1, 2015, pages 970–975. IEEE Computer Society.
[34] Tien, Y., Hsu, C., and Yu, F. (2019). Hiseqgan: Hierarchical sequence synthesis and prediction. In Tetko, I. V., Kurková, V., Karpov, P., and Theis, F. J., editors,
Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II, volume 11728 of Lecture Notes in Computer Science, pages 621–638. Springer.
[35] Vega-Márquez, B., Rubio-Escudero, C., Riquelme, J. C., and Nepomuceno-Chamorro, I. A. (2019). Creation of synthetic data with conditional generative adversarial networks. In 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) - Seville, Spain, May 13-15, 2019, Proceedings, volume 950 of Advances in Intelligent Systems and Computing, pages 231–240. Springer.
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dc.identifier.doi (DOI) 10.6814/NCCU202001449en_US