在當前技術進步的背景下，我們能夠以無前例的方式來收集和分析時序數據，這對各種領域都極具價值。儘管如此，傳統的時序數據分析方法通常依賴於專家的深入知識。這項研究提出了一種基於Shapelet演變圖的新型時序數據分析方法，該方法旨在直觀地捕捉數據的核心模式和特征，而無需專家介入。進一步的比較分析顯示，我們的方法在存在明確的pattern transform情境中表現出色。此外，我們的研究不僅為時序數據提供了全新的分析角度和方法，而且透過與其他基線方法的比較，為我們提供了預測數據集是否存在pattern transform現象的基礎知識。

Against the backdrop of technological advancements, we are now equipped to collect and analyze time series data in unparalleled ways, offering significant value across various fields. However, traditional time series data analysis often leans heavily on expert insight. This study introduces a novel approach to time series data analysis based on the shapelet evolution graph, designed to intuitively capture core patterns and characteristics within data without the need for expert intervention. Further comparative analysis reveals that our approach excels in scenarios with explicit pattern transitions. Moreover, our research not only offers a fresh perspective and methodology for time series data analysis, but, through comparison with other baseline methods, provides foundational knowledge to predict whether a dataset exhibits pattern transition phenomena.

[1] S. Huang, Y. Guo, D. Liu, S. Zha, and W. Fang, “A two-stage transfer learning-based deep learning approach for production progress prediction in iot-enabled manufacturing,” IEEE Internet of Things Journal, vol. 6, no. 6, pp. 10 627–10 638, 2019.
[2] A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. Liu, J. Marcus, M. Sun et al., “Scalable and accurate deep learning with electronic health records,” NPJ digital medicine, vol. 1, no. 1, p. 18, 2018.
[3] Z. Wu, Y. Mu, S. Deng, and Y. Li, “Spatial–temporal short-term load forecasting framework via k-shape time series clustering method and graph convolutional networks,” Energy Reports, vol. 8, pp. 8752–8766, 2022.
[4] D. Alberg and M. Last, “Short-term load forecasting in smart meters with sliding window-based arima algorithms,” Vietnam Journal of Computer Science, vol. 5, pp. 241–249, 2018.
[5] M. A. Devlin and B. P. Hayes, “Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data,” IEEE transactions on consumer electronics, vol. 65, no. 3, pp. 339–348, 2019.
[6] M. A. Devlin and B. P. Hayes, “Load identification and classification of activities of daily living using residential smart meter data,” in 2019 IEEE Milan PowerTech. IEEE, 2019, pp. 1–6.
[7] L. Ye and E. Keogh, “Time series shapelets: a novel technique that allows accurate, interpretable and fast classification,” Data mining and knowledge discovery, vol. 22, pp. 149–182, 2011.
[8] W. Yan, G. Li, Z. Wu, S. Wang, and P. S. Yu, “Extracting diverse-shapelets for early classification on time series,” World Wide Web, vol. 23, pp. 3055–3081, 2020.
[9] Z. Cheng, Y. Yang, W. Wang, W. Hu, Y. Zhuang, and G. Song, “Time2graph: Revisiting time series modeling with dynamic shapelets,” in Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 04, 2020, pp. 3617–3624. 30
[10] L. Ye and E. Keogh, “Time series shapelets: a new primitive for data mining,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 947–956.
[11] S. Aghabozorgi, A. S. Shirkhorshidi, and T. Y. Wah, “Time-series clustering–a decade review,” Information systems, vol. 53, pp. 16–38, 2015.
[12] A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh, “The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances,” Data mining and knowledge discovery, vol. 31, pp. 606–660, 2017.
[13] P.-E. Danielsson, “Euclidean distance mapping,” Computer Graphics and image processing, vol. 14, no. 3, pp. 227–248, 1980.
[14] M. M¨uller, “Dynamic time warping,” Information retrieval for music and motion, pp. 69–84, 2007.
[15] Y.-S. Jeong, M. K. Jeong, and O. A. Omitaomu, “Weighted dynamic time warping for time series classification,” Pattern recognition, vol. 44, no. 9, pp. 2231–2240, 2011.
[16] P.-F. Marteau, “Time warp edit distance with stiffness adjustment for time series matching,” IEEE transactions on pattern analysis and machine intelligence, vol. 31, no. 2, pp. 306–318, 2008.
[17] A. Stefan, V. Athitsos, and G. Das, “The move-split-merge metric for time series,”IEEE transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp. 1425–1438, 2012.
[18] G. E. Batista, E. J. Keogh, O. M. Tataw, and V. M. De Souza, “Cid: an efficient complexity-invariant distance for time series,” Data Mining and Knowledge Discovery, vol. 28, pp. 634–669, 2014.
[19] T. G´orecki and M. Luczak, “Using derivatives in time series classification,” Data Mining and Knowledge Discovery, vol. 26, pp. 310–331, 2013.
[20] T. G´orecki and M. Luczak, “Non-isometric transforms in time series classification using dtw,” Knowledge-based systems, vol. 61, pp. 98–108, 2014.
[21] A. Abanda, U. Mori, and J. A. Lozano, “A review on distance based time series classification,” Data Mining and Knowledge Discovery, vol. 33, no. 2, pp. 378–412, 2019.31
[22] J. Wu, L. Yao, and B. Liu, “An overview on feature-based classification algorithms for multivariate time series,” in 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2018, pp. 32–38.
[23] T. Chen, T. He, M. Benesty, V. Khotilovich, Y. Tang, H. Cho, K. Chen, R. Mitchell, I. Cano, T. Zhou et al., “Xgboost: extreme gradient boosting,” R package version 0.4-2, vol. 1, no. 4, pp. 1–4, 2015.
[24] J. Lin, R. Khade, and Y. Li, “Rotation-invariant similarity in time series using bagof-patterns representation,” Journal of Intelligent Information Systems, vol. 39, pp.287–315, 2012.
[25] H. Deng, G. Runger, E. Tuv, and M. Vladimir, “A time series forest for classification and feature extraction,” Information Sciences, vol. 239, pp. 142–153, 2013.
[26] J. Lines and A. Bagnall, “Time series classification with ensembles of elastic distance measures,” Data Mining and Knowledge Discovery, vol. 29, pp. 565–592, 2015.
[27] P. Senin and S. Malinchik, “Sax-vsm: Interpretable time series classification using sax and vector space model,” in 2013 IEEE 13th international conference on data mining. IEEE, 2013, pp. 1175–1180.
[28] J. Grabocka, N. Schilling, M. Wistuba, and L. Schmidt-Thieme, “Learning timeseries shapelets,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 392–401.
[29] T. Rakthanmanon and E. Keogh, “Fast shapelets: A scalable algorithm for discovering time series shapelets,” in proceedings of the 2013 SIAM International Conference on Data Mining. SIAM, 2013, pp. 668–676.
[30] B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 701–710.
[31] Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen, and G. Batista, “The ucr time series classification archive,” July 2015, www.cs.ucr.edu/∼eamonn/time series data/