Machine Learning and Information Retrieval Techniques for Time Series Analysis

• Bionda Rozin UNESP
• Daniel Carlos Guimarães Pedronette UNESP

Resumo

This work explores the intersection of Machine Learning and Information Retrieval for time series analysis, addressing key challenges in representation, classification, clustering, and retrieval. Four methodologies are proposed, covering univariate and multivariate time series across supervised, unsupervised, and semi-supervised scenarios. The approaches integrate contextual similarity learning, image-based representations, and domain-specific graph modeling, demonstrating competitive performance across multiple datasets.

Referências

K. Ratna Prakarsha and G. Sharma, “Time series signal forecasting using artificial neural networks: An application on ecg signal,” Biomedical Signal Processing and Control, vol. 76, p. 103705, 2022. [Online]. Available: [link]

D. Zhu, D. Song, Y. Chen, C. Lumezanu, W. Cheng, B. Zong, J. Ni, T. Mizoguchi, T. Yang, and H. Chen, “Deep unsupervised binary coding networks for multivariate time series retrieval,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 02, pp. 1403–1411, Apr. 2020. [Online]. Available: [link]

J. Y. Campbell and N. G. Mankiw, “Consumption, Income and Interest Rates: Reinterpreting the Time Series Evidence,” in NBER Macroeconomics Annual 1989, V. 4. National Bureau of Economic Research, Inc, November 1989, pp. 185–246. [Online]. Available: [link]

D. Song, N. Xia, W. Cheng, H. Chen, and D. Tao, “Deep r -th root of rank supervised joint binary embedding for multivariate time series retrieval,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, ser. KDD ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 2229–2238. [Online]. DOI: 10.1145/3219819.3220108

P. Hewage, A. Behera, M. Trovati, E. Pereira, M. Ghahremani, F. Palmieri, and Y. Liu, “Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station,” Soft Computing, vol. 24, no. 21, pp. 16 453–16 482, Nov 2020. [Online]. DOI: 10.1007/s00500-020-04954-0

M. Mudassir, S. Bennbaia, D. Ünal, and M. Hammoudeh, “Time-series forecasting of bitcoin prices using high-dimensional features: a machine learning approach,” Neural Computing and Applications, 07 2020.

Z. Zeng, T. Balch, and M. Veloso, “Deep video prediction for time series forecasting,” in Proceedings of the Second ACM International Conference on AI in Finance, ser. ICAIF ’21. New York, NY, USA: Association for Computing Machinery, 2022. [Online]. DOI: 10.1145/3490354.3494404

H. Zhu, P. Zhao, Y.-P. Chan, H. Kang, and D. L. Lee, “Breast cancer early detection with time series classification,” in Proceedings of the 31st ACM International Conference on Information amp; Knowledge Management, ser. CIKM ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 3735–3745. [Online]. DOI: 10.1145/3511808.3557107

Z. Zhang, D. Li, Z. Zhang, and N. Duffield, “A time-series clustering algorithm for analyzing the changes of mobility pattern caused by covid-19,” in Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility, ser. HANIMOB ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 13–17. [Online]. DOI: 10.1145/3486637.3489489

A. Abdoli, A. C. Murillo, C.-C. M. Yeh, A. C. Gerry, and E. J. Keogh, “Time series classification to improve poultry welfare,” in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 635–642.

L. P. Valem, D. C. G. Pedronette, and L. J. Latecki, “Rank flow embedding for unsupervised and semi-supervised manifold learning,” IEEE Transactions on Image Processing, vol. 32, pp. 2811–2826, 2023. [Online]. DOI: 10.1109%2Ftip.2023.3268868

J. Almeida, D. C. G. Pedronette, B. C. Alberton, L. P. C. Morellato, and R. d. S. Torres, “Unsupervised distance learning for plant species identification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 12, pp. 5325–5338, 2016.

S. Pailot-Bonnétat, A. J. L. Harris, S. Calvari, M. De Michele, and L. Gurioli, “Plume height time-series retrieval using shadow in single spatial resolution satellite images,” Remote Sensing, vol. 12, no. 23, 2020. [Online]. Available: [link]

B. Rozin, D. C. G. Pedronette, and R. S. Torres, “Re-ranking and representations for time series retrieval: A comparative study (in major revision),” IEEE Access, 2025.

B. Rozin and D. C. G. Pedronette, “A ranked-based framework based on manifold learning for multivariate time series retrieval and classification (in major revision),” Pattern Recognition Letters, 2025.

B. Rozin, R. S. Torres, F. A. Moura, and D. C. G. Pedronette, “Ball possession analysis based on temporal network properties (in revision),” Multimedia Tools and Applications, 2025.

B. Rozin, E. Bergamim, D. C. G. Pedronette, and F. A. Breve, “Semi-supervised time series classification through image representations,” in International Conference on Computational Science and Its Applications – ICCSA 2023, O. Gervasi, B. Murgante, D. Taniar, B. O. Apduhan, A. C. Braga, C. Garau, and A. Stratigea, Eds. Cham: Springer Nature Switzerland, 2023, pp. 48–65. [Online]. DOI: 10.1007/978-3-031-36808-0_4

C. Ji, M. Du, Y. Hu, S. Liu, L. Pan, and X. Zheng, “Time series classification based on temporal features,” Applied Soft Computing, vol. 128, p. 109494, 2022. [Online]. Available: [link]

D. Quoc Nguyen, M. Nguyet Phan, and I. Zelinka, “Periodic time series forecasting with bidirectional long short-term memory: Periodic time series forecasting with bidirectional lstm,” in 2021 The 5th International Conference on Machine Learning and Soft Computing, ser. ICMLSC’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 60–64. [Online]. DOI: 10.1145/3453800.3453812

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.

R. D. S. Torres and A. X. Falcão, “Content-based image retrieval: Theory and applications,” Revista de Informática Teórica e Aplicada, vol. 13, pp. 161–185, 2006.