Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets
It addresses robust prediction in periodical time-series for applications such as mobility and energy management, but appears incremental as it builds on convolutional neural networks with temporal embeddings.
The paper tackles the challenge of predicting periodical time-series with distortions and misalignments by proposing TeNet, a model that learns hidden structural elements called abstract snippets, showing significant advantages over existing methods across various data modalities like human mobility and power consumption.
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.