Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile
Spatiotemporal data are very common in many applications, such as manufacturing systems and transportation systems. Given the intrinsic complex spatial and temporal correlations of such data, short-term and long-term prediction for spatiotemporal data is often very challenging. Most of the tradition...
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| Vydáno v: | IEEE robotics and automation letters Ročník 5; číslo 4; s. 5010 - 5017 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | Spatiotemporal data are very common in many applications, such as manufacturing systems and transportation systems. Given the intrinsic complex spatial and temporal correlations of such data, short-term and long-term prediction for spatiotemporal data is often very challenging. Most of the traditional statistical models fail to preserve innate features in data alongside their complex correlations. In this paper, we focus on a tensor-based prediction method and propose several practical techniques to improve both long-term and short-term prediction accuracy. For long-term prediction, we propose the "tensor decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)" model, and an effective way to update prediction in real-time; For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplification and ensure accuracy. A case study based on the metro passenger flow data is conducted to demonstrate the improved performance. |
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| AbstractList | Spatiotemporal data are very common in many applications, such as manufacturing systems and transportation systems. Given the intrinsic complex spatial and temporal correlations of such data, short-term and long-term prediction for spatiotemporal data is often very challenging. Most of the traditional statistical models fail to preserve innate features in data alongside their complex correlations. In this paper, we focus on a tensor-based prediction method and propose several practical techniques to improve both long-term and short-term prediction accuracy. For long-term prediction, we propose the "tensor decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)" model, and an effective way to update prediction in real-time; For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplification and ensure accuracy. A case study based on the metro passenger flow data is conducted to demonstrate the improved performance. |
| Author | Zhang, Chen Yan, Hao Tsung, Fugee Li, Ziyue |
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| SubjectTerms | Autoregressive models Autoregressive moving average Autoregressive moving-average models Bayes methods big data in robotics and automation Clustering Correlation Flow profiles Intelligent transportation system Mathematical analysis Matrix decomposition Passengers Predictive models probability and statistical methods Short term Spatiotemporal phenomena Statistical models Tensors Transportation systems Two dimensional models |
| Title | Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile |
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