Traffic Forecasting via Dilated Temporal Convolution With Peak-Sensitive Loss
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DOI: 10.1109/mits.2021.3119869
Abstract: Deep learning-based traffic forecasting methods can capture intricate spatiotemporal features in traffic data and environmental factors. However, they have unsatisfactory performance around the minority peaks and are inefficient for modeling wide-range spatial correlations. This article… read more here.
Keywords: sensitive loss; traffic forecasting; peak sensitive; loss ... See more keywords