Earliness-Aware Deep Convolutional Networks for Early Time Series Classification
This addresses the problem of making accurate predictions from incomplete time series data for applications requiring early decisions, representing a novel approach rather than an incremental improvement.
The paper tackles early time series classification by proposing Earliness-Aware Deep Convolutional Networks (EA-ConvNets), which jointly learn deep shapelet features and dynamically truncate time series to focus on early parts, resulting in highly reliable predictions that outperform state-of-the-art early classification methods and are competitive with full-length methods.
We present Earliness-Aware Deep Convolutional Networks (EA-ConvNets), an end-to-end deep learning framework, for early classification of time series data. Unlike most existing methods for early classification of time series data, that are designed to solve this problem under the assumption of the availability of a good set of pre-defined (often hand-crafted) features, our framework can jointly perform feature learning (by learning a deep hierarchy of \emph{shapelets} capturing the salient characteristics in each time series), along with a dynamic truncation model to help our deep feature learning architecture focus on the early parts of each time series. Consequently, our framework is able to make highly reliable early predictions, outperforming various state-of-the-art methods for early time series classification, while also being competitive when compared to the state-of-the-art time series classification algorithms that work with \emph{fully observed} time series data. To the best of our knowledge, the proposed framework is the first to perform data-driven (deep) feature learning in the context of early classification of time series data. We perform a comprehensive set of experiments, on several benchmark data sets, which demonstrate that our method yields significantly better predictions than various state-of-the-art methods designed for early time series classification. In addition to obtaining high accuracies, our experiments also show that the learned deep shapelets based features are also highly interpretable and can help gain better understanding of the underlying characteristics of time series data.