Abstract
The access to sufficient amount of data has always challenged researchers to productively effectuate their solutions. One of the promising solutions can be generation of data using generative adversarial networks (GAN). This paper is focused on generating realistic data for different time series applications using GAN. The approach adopted here uses GRU-based GAN with conditional input for data generation. The data generated using GAN can contribute in the formation of larger datasets. The time component plays a major role in forecasting in various domains so it is crucial to target data related to time series. The competence of the data generated has been judged by using it to train the most prominent time series forecasting models and then testing it using real data. The linear regression model, ARIMA model, and GRU-based forecasting model are chosen for carrying out the experiment. The similarity between the actual data and generated data is also demonstrated using Wilcoxon signed-rank test as the datasets used here are nonparametric. The experimentation has been executed on three real world datasets from different domains.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shekhawat, S.S., Sharma, H., Kumar, S., Nayyar, A., Qureshi, B.: bssA: binary salp swarm algorithm with hybrid data transformation for feature selection. IEEE Access 9, 14867–14882 (2021)
Esteban, C., Hyland, S.L., Rätsch, G.: Real-valued (medical) time series generation with recurrent conditional gans (2017)
Choi, E., Biswal, S., Malin, B.A., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete electronic health records using generative adversarial networks. CoRR (2017). abs/1703.06490
Goodfellow, I.J.: NIPS 2016 tutorial: generative adversarial networks. CoRR (2017). abs/1701.00160
Wang, C., Xu, C., Yao, X., Tao, D.: Evolutionary generative adversarial networks. IEEE Trans. Evol. Comput. 23(6), 921–934 (2019)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates Inc. (2014)
Zhang, C., Kuppannagari, S.R., Kannan, R., Prasanna, V.K.: Generative adversarial network for synthetic time series data generation in smart grids. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1–6, (2018)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR. abs/1511.06434 (2015)
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional gans. CoRR. abs/1711.11585, 2017
Singh, V., Poonia, R.C., Kumar, S., Dass, P., Agarwal, P., Bhatnagar, V., Raja, L.: Prediction of covid-19 corona virus pandemic based on time series data using support vector machine. J. Discrete Math. Sci. Crypt. 1–15 (2020)
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. CoRR (2016). abs/1611.07004
Viswanathan, A., Mehta, B., Bhavatarini, M.P., Mamatha. H.R.: Text to image translation using generative adversarial networks. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1648–1654 (2018, September)
Karras, T., Aila, T., Laine, S., Lehtinen, S.: Progressive growing of gans for improved quality, stability, and variation. CoRR. abs/1710.10196, 2017
Jin, Y., Zhang, J., Li, M., Tian, Y., Zhu, H., Fang, Z.: Towards the automatic anime characters creation with generative adversarial networks. CoRR. abs/1708.05509, 2017
Gadelha, M., Maji, S., Wang, R.: 3d shape induction from 2d views of multiple objects. CoRR. abs/1612.05872 (2016)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR. abs/1411.1784, 2014
Antipov, G., Baccouche, M., Dugelay, J.-L.: Face aging with conditional generative adversarial networks. CoRR (2017). abs/1702.01983
Douzas, G., Bação, Fernando: Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst. Appl. 91, 9 (2017)
Haidar, M.A., Rezagholizadeh, M.: Textkd-gan: text generation using knowledge distillation and generative adversarial networks. ArXiv, abs/1905.01976 (2019)
Mogren, O.: C-RNN-GAN: continuous recurrent neural networks with adversarial training. CoRR. abs/1611.09904 (2016)
Zhou, X., Pan, Z., Hu, G., Tang, S., Zhao, C.: Stock market prediction on high-frequency data using generative adversarial nets. Math. Prob. Eng. 1–11(04), 2018 (2018)
Fu, R., Chen, J., Zeng, S., Zhuang, Y., Sudjianto, A.: Time series simulation by conditional generative adversarial net (2019). ArXiv, abs/1904.11419
Yoon, J., Jarrett, D., van der Schaar, M.: Time-series generative adversarial networks. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems vol. 32, pp. 5509–5519. Curran Associates, Inc. (2019)
Ramponi, G., Protopapas, P., Brambilla, M., Janssen, R.: T-cgan: conditional generative adversarial network for data augmentation in noisy time series with irregular sampling. ArXiv, abs/1811.08295, 2018
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.S.: Domain-adversarial Training of Neural Networks. J. Mach. Learn. Res. 17:5, 9, 1–59:35 (2015)
National renewable energy laboratory (nrel) 2007–2008 western dataset. site-id 72509
Power data access viewer, 1983–2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khare, P., Wadhvani, R., Gyanchandani, M., Brahma, B. (2022). Generating Data for Real World Time Series Application with GRU-Based Conditional GAN. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_27
Download citation
DOI: https://doi.org/10.1007/978-981-16-5348-3_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5347-6
Online ISBN: 978-981-16-5348-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)