PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series
This addresses the challenge of modeling multi-source time-series with irregular patterns for applications like pandemic forecasting, though it appears incremental as it builds on existing irregularity handling techniques.
The authors tackled the problem of forecasting with heterogeneous and irregular multi-source time-series by proposing PIETS, a novel architecture that uses parallelized irregularity encoders and attention mechanisms, and demonstrated it outperforms state-of-the-art methods on real-world COVID-19 datasets.
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.