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• W2912816933 abstract "Spatial time series forecasting problems arise in a broad range of applications, such as environmental and transportation problems. These problems are challenging because of the existence of specific spatial, short-term and long-term patterns, and the curse of dimensionality. In this paper, we propose a deep neural network framework for large-scale spatial time series forecasting problems. We explicitly designed the neural network architecture for capturing various types of patterns. In preprocessing, a time series decomposition method is applied to separately feed short-term, long-term and spatial patterns into different components of a neural network. A fuzzy clustering method finds cluster of neighboring time series based on similarity of time series residuals; as they can be meaningful short-term patterns for spatial time series. 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. The output of convolution layer is concatenated by trends and followed by convolution-LSTM layer to capture long-term patterns in larger regional areas. To make a robust prediction when faced with missing data, an unsupervised pretrained denoising autoencoder reconstructs the output of the model in a fine-tuning step. The experimental results illustrate the model outperforms baseline and state of the art models in a traffic flow prediction dataset." @default.
• W2912816933 created "2019-02-21" @default.
• W2912816933 creator A5023408475 @default.
• W2912816933 creator A5045530260 @default.
• W2912816933 date "2019-02-02" @default.
• W2912816933 modified "2023-09-26" @default.
• W2912816933 title "A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting." @default.
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• W2912816933 hasPublicationYear "2019" @default.
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