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- W2895854315 abstract "Recently, the scale of observed time series becomes bigger and bigger, and the learning ability of conventional models is limited to depict the characteristics of large-scale time series. We put forward a deep model that uses the sparse autoencoder to extract the characteristics of the time series layer by layer. Sparse autoencoder can provide a simple explanation of the data by means of a reduced dimensionality of parts and extract the hidden architecture in the data. By this method, on the one hand, the dimension of the matrix that describes the problem is reduced, on the other hand, a large number of data can be compressed and summarized. Then we calculate the output weights by the method of pseudo inverse. Experiment results on Lorenz series and PM2.5 concentration in Shanghai dataset demonstrate the effectiveness of the proposed method." @default.
- W2895854315 created "2018-10-26" @default.
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- W2895854315 date "2018-07-01" @default.
- W2895854315 modified "2023-10-16" @default.
- W2895854315 title "A Novel Time Series Prediction Model Based on Deep Sparse Autoencoder" @default.
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- W2895854315 doi "https://doi.org/10.23919/chicc.2018.8482990" @default.
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