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• W3204321804 abstract "Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model; (2) parallelised neural networks to enable flexibility and avoid information overwhelming; and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task." @default.
• W3204321804 created "2021-10-11" @default.
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• W3204321804 date "2021-12-01" @default.
• W3204321804 modified "2023-10-18" @default.
• W3204321804 title "PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series" @default.
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• W3204321804 doi "https://doi.org/10.1109/icdm51629.2021.00109" @default.
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