Abstract
Characteristics at nodes in a network, such as values of demand, evolve over time. To make time-dependent decisions for a network, making time series predictions at each node in the network over time is often necessary. Typical time series prediction approaches are based on historical information. However, these fail to account for network-level factors that might affect nodal values. This paper proposes an approach for the time series prediction in nodal networks that accounts for both time history information and nodal characteristics in the prediction. The approach is based on recurrent neural networks and, in particular, gated recurrent units (GRU), creating a new GRU structure called a Pairwise-GRU to include the influence of both historical data and neighboring node information to predict values at each node in the network. The result is a more accurate and confident time series prediction. The performance of the proposed approach is tested using an electricity network in the southeastern United States. The results indicate that the proposed Pairwise-GRU outperforms existing methods in terms of increased accuracy and decreased uncertainty in the prediction. The approach performs particularly well for long-term, multiple-time-steps ahead predictions and anomalous hazard conditions in addition to normal operating scenarios.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author on reasonable request.
References
Althelaya, K. A., M. El-Alfy El-Sayed, and S. Mohammed. 2018. “Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU).” In Proc., 21st Conf. of Saudi Computer Society National Computer (NCC), 1–7. New York: IEEE.
Bai, Y., P. Wang, C. Li, J. Xie, and Y. Wang. 2014. “A multi-scale relevance vector regression approach for daily urban water demand forecasting.” J. Hydrol. 517 (Sep): 236–245. https://doi.org/10.1016/j.jhydrol.2014.05.033.
Cho, K., B. Merrienboer, C. Culcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. 2014. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” Preprint, submitted June 6, 2014. http://arxiv.org/abs/1406.1708.
Deng, J. 1982. “Control problems of grey systems.” Syst. Control Lett. 1 (5): 288–294. https://doi.org/10.1016/S0167-6911(82)80025-X.
Frank, R. J., N. Davey, and S. P. Hunt. 2001. “Time series prediction and neural networks.” J. Intell. Rob. Syst. 31 (1): 91–103. https://doi.org/10.1023/A:1012074215150.
Gonzalez-Abril, L., J. M. Gavilan, and F. Velasco Morente. 2014. “Three similarity measures between one-dimensional dataSets.” Revista Colombiana de Estadística 37 (1): 79–94. https://doi.org/10.15446/rce.v37n1.44359.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 8 (9): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Holzfuss, J., and G. Mayer-Kress. 1986. “An approach to error-estimation in the application of dimension algorithms.” Dimens. Entropies Chaotic Syst. 17 (13): 114–122. https://doi.org/10.1007/978-3-642-71001-8_15.
Hu, J., and W. Zheng. 2019. “Transformation-gated LSTM: Efficient capture of short-term mutation dependencies for multivariate time series prediction tasks.” In Proc., Int. Joint Conf. on Neural Networks (IJCNN), 1–8. Mumbai, India: Tata Consultancy Services.
Iglesias, G., and W. Wastner. 2013. “Analysis of similarity measures in time series clustering for the discovery of building energy patterns.” Energies 6 (2): 579–597. https://doi.org/10.3390/en6020579.
Jang, Y., I. Jeong, and Y. Cho. 2019. “Business failure prediction with LSTM RNN in the construction industry.” In Proc., Int. Conf. on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, 114–121. Reston, VA: ASCE.
Kayacan, E., U. Baris, and O. Kaynak. 2010. “Grey system theory-based models in time series prediction.” Expert Syst. Appl. 7 (2): 1784–1789. https://doi.org/10.1016/j.eswa.2009.07.064.
Li, S., W. Li, C. Cook, C. Zhu, and Y. Gao. 2018. “Independently recurrent neural network (IndRNN): Building a longer and deeper RNN.” In Proc., IEEE Conf., on Computer Vision and Pattern Recognition (CVPR), 5457–5466. New York: IEEE.
National Weather Service. 2019. “December 2019 South Florida flooding.” Accessed July 1, 2020. https://www.weather.gov/mfl/dec2019flooding.
Nikolaev, N. Y., and H. Iba. 2003. “Polynomial harmonic GMDH learning networks for time series modeling.” Neural Netw. 16 (10): 1527–1540. https://doi.org/10.1016/S0893-6080(03)00188-6.
Sapankevych, N. I., and R. Sankar. 2009. “Time series prediction using support vector machines: A survey.” IEEE Comput. Intell. Mag. 4 (2): 24–38. https://doi.org/10.1109/MCI.2009.932254.
Shahin, M. A. 2014. “Load-settlement modeling of axially loaded drilled shafts using CPT-based recurrent neural networks.” Int. J. Geomech. 14 (6): 06014012. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000370.
Shelekhova, V. Y. 1995. “Harmonic algorithm GMDH for large data volume.” Syst. Anal. Modell. Simul. 20 (1): 117–126. https://doi.org/10.5555/214610.214620.
Tipping, M. 2001. “Sparse Bayesian learning and the relevance vector machine.” J. Mach. Learn. Res. 1 (Jun): 211–244. https://doi.org/10.1162/15324430152748236.
U.S. Energy Information Administration. 2020. “Florida state profile and energy estimates.” Accessed July 1 2020. https://www.eia.gov.
Wei, X., L. Zhang, H. Yang, L. Zhang, and Y. Yao. 2021. “Machine learning for pore-water pressure time series prediction: Application of recurrent neural networks.” Geosci. Front. 12 (1): 453–467. https://doi.org/10.1016/j.gsf.2020.04.011.
Xu, N., and X. Zhang. 2010. “Traffic volume prediction based on improved grey self-adaptable prediction formula.” In Proc., 9th Int. Conf., on Machine Learning and Cybernetics, Qingdao, 11–14. New York: IEEE.
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Received: Jun 16, 2021
Accepted: Dec 1, 2021
Published online: Jan 17, 2022
Published in print: Jun 1, 2022
Discussion open until: Jun 17, 2022
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