PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series
21st IEEE International Conference on Data Mining (IEEE ICDM)
; : 976-981, 2021.
Article
in English
| Web of Science | ID: covidwho-1806912
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 over-whelming;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.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Experimental Studies
Language:
English
Journal:
21st IEEE International Conference on Data Mining (IEEE ICDM)
Year:
2021
Document Type:
Article
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