Abstract
Neural networks (NNs) have recently achieved significant performance gains for time series forecasting. However, they require large amounts of data to train. Data augmentation techniques have been suggested to improve the network training performance. Here, we adopt Empirical Mode Decomposition (EMD) as a data augmentation technique. The intrinsic mode functions (IMFs) produced by EMD are recombined with different weights to obtain surrogate data series, which are used to train a neural network for forecasting. We use M4 time series dataset and a custom nonlinear auto-regressive network with exogenous inputs (NARX) for the validation of the proposed method. The experimental results show an improvement in forecasting accuracy over a baseline method when using EMD based data augmentation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Capizzi, G., Napoli, C., Bonanno, F.: Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting. IEEE Trans. Neural Netw. Learn. Syst. 23(11), 1805–1815 (2012)
Zȩbik, M., Korytkowski, M., Angryk, R., Scherer, R.: Convolutional neural networks for time series classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, Lotfi A., Zurada, Jacek M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10246, pp. 635–642. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59060-8_57
Qi, G.-J., Luo, J.: Small data challenges in big data era: a survey of recent progress on unsupervised and semi-supervised methods. CoRR abs/1903.11260 (2019)
Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: Interdisciplinary PhD Workshop, pp. 117–122. (2018)
Park, D.S., et al.: Specaugment: a simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779 (2019)
Eyobu, S.O., Han, D.S.: Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network. Sensors 18(9), 2892 (2018)
le Guennec, A., Malinowski, S., Tavenard, R.: Data augmentation for time series classification using convolutional neural networks. In: Proceedings of the ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Porto, Portugal (2016)
Um, T.T., et al.: Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks. In: 19th ACM International Conference on Multimodal Interaction; Glasgow, UK, 13–17 November (2017)
Bergmeir, C., Hyndman, R.J., Benítez, J.M.: Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. Int. J. Forecast. 32(2), 303–312 (2016)
Paparoditis, E., Politis, D.: Tapered block bootstrap. Biometrika 88(4), 1105–1119 (2001)
Shao, X.: The dependent wild bootstrap. J. Am. Stat. Assoc. 105(489), 218–235 (2010)
Maiwald, T., Mammen, E., Nandi, S., Timmer, J.: Surrogate data - a qualitative and quantitative analysis. In: Dahlhaus, R., et al. (eds.) Mathematical Methods in Signal Processing and Digital Image Analysis. Understanding Complex Systems. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-75632-3_2
Prichard, D., Theiler, J.: Generating surrogate data for time series with several simultaneously measured variables. Phys. Rev. Lett. 73(7), 951–954 (1994)
Kaefer, P.E., Ishola, B.I., Corliss, G.F., Brown, R.H.: Using surrogate data to mitigate the risks of natural gas forecasting on unusual days. In: 35th International Symposium on Forecasting (2015)
Duncan, G.T., Gorr, W.L., Szczypula, J.: Forecasting analogous time series. In: Principles of Forecasting: A Handbook for Researchers and Practitioners, pp. 195–213 (2001)
Schreiber, T., Schmitz, A.: Surrogate time series. Physica D 142, 346–382 (1999)
Sidekerskienė, T., Woźniak, M., Damaševičius, R.: Nonnegative matrix factorization based decomposition for time series modelling. In: Saeed, K., Homenda, W., Chaki, R. (eds.) CISIM 2017. LNCS, vol. 10244, pp. 604–613. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59105-6_52
Sidekerskiene, T., Damasevicius, R., Wozniak, M.: Zerocross density decomposition: a novel signal decomposition method. In: Dzemyda, G., et al. (eds.) Data Science: New Issues, Challenges and Applications. Studies Comp. Intelligence, vol. 869 (2020)
Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454(1971), pp. 903–995 (1998)
Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The M4 Competition: 100,000 time series and 61 forecasting methods. Int. J. Forecast. 36(1), 54–74 (2020)
Horzyk, A., Starzyk, J.A.: Associative data model in search for nearest neighbors and similar patterns. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 933–940. IEEE, December 2019
Shewalkar, A., Nyavanandi, D., Ludwig, S.A.: Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. J. Artif. Intell. Soft Comput. Res. 9(4), 235–245 (2019)
Nobukawa, S., Nishimura, H., Yamanishi, T.: Pattern classification by spiking neural networks combining self-organized and reward-related spike-timing-dependent plasticity. J. Artif. Intell. Soft Comput. Res. 9(4), 283–291 (2019)
Acknowledgments
Authors acknowledge contribution to this project of the Program “Best of the Best 4.0” from the Polish Ministry of Science and Higher Education No. MNiSW/2020/43/DIR/NN4.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Abayomi-Alli, O.O., Sidekerskienė, T., Damaševičius, R., Siłka, J., Połap, D. (2020). Empirical Mode Decomposition Based Data Augmentation for Time Series Prediction Using NARX Network. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_65
Download citation
DOI: https://doi.org/10.1007/978-3-030-61401-0_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61400-3
Online ISBN: 978-3-030-61401-0
eBook Packages: Computer ScienceComputer Science (R0)