The prediction of periodical time-series remains challenging due to various
types of data distortions and misalignments. Here, we propose a novel model
called Temporal embedding-enhanced convolutional neural Network (TeNet) to
learn repeatedly-occurring-yet-hidden structural elements in periodical
time-series, called abstract snippets, for predicting future changes. Our model
uses convolutional neural networks and embeds a time-series with its potential
neighbors in the temporal domain for aligning it to the dominant patterns in
the dataset. The model is robust to distortions and misalignments in the
temporal domain and demonstrates strong prediction power for periodical
time-series.
We conduct extensive experiments and discover that the proposed model shows
significant and consistent advantages over existing methods on a variety of
data modalities ranging from human mobility to household power consumption
records. Empirical results indicate that the model is robust to various factors
such as number of samples, variance of data, numerical ranges of data etc. The
experiments also verify that the intuition behind the model can be generalized
to multiple data types and applications and promises significant improvement in
prediction performances across the datasets studied.

本文提出了一种基于时间嵌入的卷积神经网络模型 (TeNet)，通过对周期性时间序列中反复发生但被隐藏的结构元素 —— 抽象片段进行学习来预测未来的变化，该模型能够对时间序列中的数据扭曲和错位具有鲁棒性，并在人类移动性到家庭用电记录等多种数据模态上展示出优异的预测性能。