Abstract
This work presents a novel ordinal Deep Learning (DL) approach to Time Series Ordinal Classification (TSOC) field. TSOC consists in classifying time series with labels showing a natural order between them. This particular property of the output variable should be exploited to boost the performance for a given problem. This paper presents a novel DL approach in which time series are encoded as 3-channels images using Gramian Angular Field and Markov Transition Field. A soft labelling approach, which considers the probabilities generated by a unimodal distribution for obtaining soft labels that replace crisp labels in the loss function, is applied to a ResNet18 model. Specifically, beta and triangular distributions have been applied. They have been compared against three state-of-the-art deep learners in the Time Series Classification (TSC) field using 13 univariate and multivariate time series datasets. The approach considering the triangular distribution (O-GAMTF\(_\text {T}\)) outperforms all the techniques benchmarked.
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References
Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A., Hervás-Martínez, C.: Assessing the efficient market hypothesis for cryptocurrencies with high-frequency data using time series classification. In: García Bringas, P., et al (eds.) 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. LNCS, vol. 531. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-18050-7_14
Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2017)
Barbero-Gómez, J., Gutiérrez, P.A., Vargas, V.M., Vallejo-Casas, J.A., Hervás-Martínez, C.: An ordinal CNN approach for the assessment of neurological damage in Parkinson’s disease patients. Expert Syst. Appl. 182, 115271 (2021)
Boonyakitanont, P., Lek-Uthai, A., Chomtho, K., Songsiri, J.: A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed. Signal Process. Control 57, 101702 (2020)
Cruz-Ramírez, M., Hervás-Martínez, C., Sánchez-Monedero, J., Gutiérrez, P.A.: Metrics to guide a multi-objective evolutionary algorithm for ordinal classification. Neurocomputing 135, 21–31 (2014)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Foumani, N.M., Miller, L., Tan, C.W., Webb, G.I., Forestier, G., Salehi, M.: Deep learning for time series classification and extrinsic regression: a current survey. arXiv preprint arXiv:2302.02515 (2023)
Guijo-Rubio, D., Gutiérrez, P., Casanova-Mateo, C., Sanz-Justo, J., Salcedo-Sanz, S., Hervás-Martínez, C.: Prediction of low-visibility events due to fog using ordinal classification. Atmos. Res. 214, 64–73 (2018)
Guijo-Rubio, D., Gutiérrez, P.A., Bagnall, A., Hervás-Martínez, C.: Time series ordinal classification via shapelets. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)
Guijo-Rubio, D., Gutiérrez, P.A., Bagnall, A., Hervás-Martínez, C.: Ordinal versus nominal time series classification. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds.) AALTD 2020. LNCS (LNAI), vol. 12588, pp. 19–29. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65742-0_2
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1
Ismail Fawaz, H., et al.: InceptionTime: finding AlexNet for time series classification. Data Min. Knowl. Disc. 34(6), 1936–1962 (2020)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3, 263–286 (2001)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Liu, P., Sun, X., Han, Y., He, Z., Zhang, W., Wu, C.: Arrhythmia classification of LSTM autoencoder based on time series anomaly detection. Biomed. Signal Process. Control 71, 103228 (2022)
Rosati, R., et al.: A novel deep ordinal classification approach for aesthetic quality control classification. Neural Comput. Appl. 34(14), 11625–11639 (2022)
de la Torre, J., Puig, D., Valls, A.: Weighted kappa loss function for multi-class classification of ordinal data in deep learning. Pattern Recogn. Lett. 105, 144–154 (2018)
Vargas, V.M., Gutiérrez, P.A., Barbero-Gómez, J., Hervás-Martínez, C.: Soft labelling based on triangular distributions for ordinal classification. Inf. Fusion 93, 258–267 (2023)
Vargas, V.M., Gutiérrez, P.A., Hervás-Martínez, C.: Unimodal regularisation based on beta distribution for deep ordinal regression. Pattern Recogn. 122, 108310 (2022)
Wang, Z., Oates, T.: Imaging time-series to improve classification and imputation. In: 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3939–3945 (2015)
Wang, Z., Oates, T., et al.: Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Workshops at the Twenty-ninth AAAI Conference On Artificial Intelligence, vol. 1. AAAI Menlo Park, CA, USA (2015)
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578–1585. IEEE (2017)
Acknowledgements
This work has been partially subsidised by “Agencia Española de Investigación (España)” (grant ref.: PID2020-115454GB-C22 / AEI / 10.13039 / 501100011033). Víctor Manuel Vargas’s research has been subsidised by the FPU Predoctoral Program of the Spanish Ministry of Science, Innovation and Universities (MCIU), grant reference FPU18/00358. David Guijo-Rubio’s research has been subsidised by the University of Córdoba through grants to Public Universities for the requalification of the Spanish university system of the Ministry of Universities, financed by the European Union - NextGenerationEU (grant reference: UCOR01MS).
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Vargas, V.M., Ayllón-Gavilán, R., Durán-Rosal, A.M., Gutiérrez, P.A., Hervás-Martínez, C., Guijo-Rubio, D. (2023). Gramian Angular and Markov Transition Fields Applied to Time Series Ordinal Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_41
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