  Risk prediction, as a typical time series modeling problem, is usually
achieved by learning trends in markers or historical behavior from sequence
data, and has been widely applied in healthcare and finance. In recent years,
deep learning models, especially Long Short-Term Memory neural networks
(LSTMs), have led to superior performances in such sequence representation
learning tasks. Despite that some attention or self-attention based models with
time-aware or feature-aware enhanced strategies have achieved better
performance compared with other temporal modeling methods, such improvement is
limited due to a lack of guidance from global view. To address this issue, we
propose a novel end-to-end Hierarchical Global View-guided (HGV) sequence
representation learning framework. Specifically, the Global Graph Embedding
(GGE) module is proposed to learn sequential clip-aware representations from
temporal correlation graph at instance level. Furthermore, following the way of
key-query attention, the harmonic $\beta$-attention ($\beta$-Attn) is also
developed for making a global trade-off between time-aware decay and
observation significance at channel level adaptively. Moreover, the
hierarchical representations at both instance level and channel level can be
coordinated by the heterogeneous information aggregation under the guidance of
global view. Experimental results on a benchmark dataset for healthcare risk
prediction, and a real-world industrial scenario for Small and Mid-size
Enterprises (SMEs) credit overdue risk prediction in MYBank, Ant Group, have
illustrated that the proposed model can achieve competitive prediction
performance compared with other known baselines.
