Humans learn from the occurrence of events in a different place and time to
predict similar trajectories of events. We define Loosely Decoupled Timeseries
(LDT) phenomena as two or more events that could happen in different places and
across different timelines but share similarities in the nature of the event
and the properties of the location. In this work we improve on the use of
Recurring Neural Networks (RNN), in particular Long Short-Term Memory (LSTM)
networks, to enable AI solutions that generate better timeseries predictions
for LDT. We use similarity measures between timeseries based on the trends and
introduce embeddings representing those trends. The embeddings represent
properties of the event which, coupled with the LSTM structure, can be
clustered to identify similar temporally unaligned events. In this paper, we
explore methods of seeding a multivariate LSTM from time-invariant data related
to the geophysical and demographic phenomena being modeled by the LSTM. We
apply these methods on the timeseries data derived from the COVID-19 detected
infection and death cases. We use publicly available socio-economic data to
seed the LSTM models, creating embeddings, to determine whether such seeding
improves case predictions. The embeddings produced by these LSTMs are clustered
to identify best-matching candidates for forecasting an evolving timeseries.
Applying this method, we show an improvement in 10-day moving average
predictions of disease propagation at the US County level.

本文提出了一种使用循环神经网络（RNN）的改进方法，特别是使用长期短期记忆（LSTM）网络，以改进针对具有不同时间和位置的相似事件的时间序列预测。作者将相似性措施与基于趋势的时间序列中引入的嵌入相结合，这些嵌入可用于聚类以识别相似的时间序列事件，同时探讨了种子多元 LSTM 的方法，以及该方法在 COVID-19 检测感染和死亡案例的时间序列数据上的应用，取得了较好的预测效果。