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Deep Learning with Kernel Flow Regularization for Time Series Forecasting
We also compare the effect of dropout and kernel flow regularization techniques on LSTMs.
Clustering of Time Series Data with Prior Geographical Information
The results show that the proposed Spatial-DEC can find more desired spatio-temporal clusters.
A convolution recurrent autoencoder for spatio-temporal missing data imputation
When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped.
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting
In neural network architecture, each kernel of a multi-kernel convolution layer is applied to a cluster of time series to extract short-term features in neighboring areas.
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