  Time series classification (TSC), the problem of predicting class labels of
time series, has been around for decades within the community of data mining
and machine learning, and found many important applications such as biomedical
engineering and clinical prediction. However, it still remains challenging and
falls short of classification accuracy and efficiency. Traditional approaches
typically involve extracting discriminative features from the original time
series using dynamic time warping (DTW) or shapelet transformation, based on
which an off-the-shelf classifier can be applied. These methods are ad-hoc and
separate the feature extraction part with the classification part, which limits
their accuracy performance. Plus, most existing methods fail to take into
account the fact that time series often have features at different time scales.
To address these problems, we propose a novel end-to-end neural network model,
Multi-Scale Convolutional Neural Networks (MCNN), which incorporates feature
extraction and classification in a single framework. Leveraging a novel
multi-branch layer and learnable convolutional layers, MCNN automatically
extracts features at different scales and frequencies, leading to superior
feature representation. MCNN is also computationally efficient, as it naturally
leverages GPU computing. We conduct comprehensive empirical evaluation with
various existing methods on a large number of benchmark datasets, and show that
MCNN advances the state-of-the-art by achieving superior accuracy performance
than other leading methods.
