  Time series data are often obtained only within a limited time range due to
interruptions during observation process. To classify such partial time series,
we need to account for 1) the variable-length data drawn from 2) different
timestamps. To address the first problem, existing convolutional neural
networks use global pooling after convolutional layers to cancel the length
differences. This architecture suffers from the trade-off between incorporating
entire temporal correlations in long data and avoiding feature collapse for
short data. To resolve this tradeoff, we propose Adaptive Multi-scale Pooling,
which aggregates features from an adaptive number of layers, i.e., only the
first few layers for short data and more layers for long data. Furthermore, to
address the second problem, we introduce Temporal Encoding, which embeds the
observation timestamps into the intermediate features. Experiments on our
private dataset and the UCR/UEA time series archive show that our modules
improve classification accuracy especially on short data obtained as partial
time series.
