Abstract
Multi-step prediction for time series is a challenging research area with broad applications which can provide important information for relevant decision-makers. Many works extended different architecture of artificial neural networks to perform time series prediction, but they mostly only consider the time series itself, does not weigh the impact of time series of relevant factors. In this paper, a new method of time series prediction based on factor mining is proposed. By analyzing target time series, the means of discovering factors influencing time series and pinned down the most relevant factors was proposed. In the end, a method to do multi-step prediction with artificial neural networks, MTPF is proposed to conduct the time series prediction, create time series model and forecast time series. The proposed method is applied for a shipping price index time series prediction. Results show that this method can improve accuracy of prediction when compared with traditional methods.
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References
Weigend, A.S.: Time series prediction: forecasting the future and understanding the past. Santa Fe Institute Studies in the Sciences of Complexity (1994)
Gershenfeld, N.A., Weigend, A.S.: The future of time series: learning and understanding. No. 93-08-053 (1993)
Wold, H.: A study in the analysis of stationary time series, pp. 113–115 (1939)
Box, G.E.P., Jenkins, G.M.: Time series models for forecasting and control. J. Time 3(2), 199–201 (1970). San Francisco
Lowe, D.: Multi-variable functional interpolation and adaptive networks. Complex Syst. 2, 321–355 (1988)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Hrasko, R., Pacheco, A.G.C., Krohling, R.A.: Time series prediction using restricted Boltzmann machines and backpropagation. Procedia Comput. Sci. 55, 990–999 (2015)
Pisoni, E., Farina, M., Carnevale, C.: Forecasting peak air pollution levels using NARX models. Eng. Appl. Artif. Intell. 22(s4–5), 593–602 (2009)
Yu, Y., Song, J., Ren, Z.: A new hyper-parameters selection approach for support vector machines to predict time series. In: Zu, Q., Hu, B., Elçi, A. (eds.) Pervasive Computing and the Networked World, pp. 775–787. Springer, Heidelberg (2013)
Faruk, D.Ö.: A hybrid neural network and ARIMA model for water quality time series prediction. Eng. Appl. Artif. Intell. 23(4), 586–594 (2010)
Li, W., Li, X., Yao, M., et al.: Personalized fitting recommendation based on support vector regression. Hum. Centric Comput. Inf. Sci. 5(1), 21 (2015)
Barzaiq, O.O., Loke, S.W.: Personal destination pattern analysis with applications to mobile advertising. Hum. Centric Comput. Inf. Sci. 6(1), 17 (2016)
Loke, S.W.: Heuristics for spatial finding using iterative mobile crowdsourcing. Hum. Centric Comput. Inf. Sci. 6(1), 4 (2016)
Aizenberg, I., Sheremetov, L., Villa-Vargas, L.: Multilayer neural network with multi-valued neurons in time series forecasting of oil production. Neurocomputing 8495, 61–70 (2015)
Cadenas, E., Rivera, W.: Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renew. Energy 34(1), 274–278 (2009)
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Zhou, M., Liu, J., Li, F., Wang, J. (2017). Multi-step Prediction for Time Series with Factor Mining and Neural Network. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_110
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DOI: https://doi.org/10.1007/978-981-10-5041-1_110
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