Abstract
Time series prediction is not easy to achieve high accuracy. non-linear and unstable characteristics make the time series prediction difficult. The variety of dataset make the prediction result debatable. In order to solve this problem, in this paper we propose a deep learning prediction method based on decomposition, reconstruction and combination, which combines ways of communication field. The model is decomposed by Empirical Mode Decomposition, Principal Component Analysis and Long Short-Term Memory networks (EPL below). And also, the proposed interval EPL (IEPL below) improve and consummate the EPL model. The EPL and IEPL experiment results will bring average 5% higher accuracy than that of existing research.
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Acknowledgement
This research work here is supported by the Science and Technology Planning Project of Tianjin (Grant No. 17JCZDJC30700 and 17YFZCGX00610).
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Sun, H., Guo, C., Xu, J., Zhu, J., Zhang, C. (2018). A Combined Model for Time Series Prediction in Financial Markets. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_11
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DOI: https://doi.org/10.1007/978-3-319-96893-3_11
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