Abstract
Time series data exist in various systems and affect the following management and control, in which real time series data sets are often composed of multiple variables. For predicting the future of data, not only the historical value of the variable but also other implicit influence factors should be considered. Therefore, we propose a prediction method based on the convolutional neural network (CNN) and Bi-directional long short term memory (Bi-LSTM) networks with the multidimensional variable. CNN is used to learn the horizontal relationship between variables of multivariate raw data, and Bi-LSTM is used to extract temporal relationships. Experiments are carried out with Beijing meteorological data, and the results show the high prediction accuracy of wind speed and temperature data. It is indicated that the proposed model can explore effectively the features of multivariable non-stationary time series data.
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Acknowledgements
This work was supported in part by the National Key Research and Development Program of China no. 2017YFC1600605, National Natural Science Foundation of China No. 61673002, and Beijing Municipal Education Commission No. KM201910011010.
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Jin, X., Yu, X., Wang, X., Bai, Y., Su, T., Kong, J. (2020). Prediction for Time Series with CNN and LSTM. In: Wang, R., Chen, Z., Zhang, W., Zhu, Q. (eds) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering, vol 582. Springer, Singapore. https://doi.org/10.1007/978-981-15-0474-7_59
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DOI: https://doi.org/10.1007/978-981-15-0474-7_59
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