论文标题
卷积神经网络，用于可变长度时间序列的时间依赖性分类
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series
论文作者
论文摘要
时间序列数据通常仅在观察过程中的中断时仅在有限的时间范围内获得。要对这样的部分时间序列进行分类，我们需要考虑1）从2）不同时间戳绘制的可变长度数据。为了解决第一个问题，现有的卷积神经网络在卷积层之后使用全球池来取消长度差异。这种体系结构在将整个时间相关性结合到长数据中与避免用于简短数据的功能崩溃之间的权衡受到了权衡。为了解决这种权衡，我们提出了自适应多尺度池，该池汇总了自适应数量的层中的特征，即仅用于简短数据的前几层和长数据的更多层。此外，为了解决第二个问题，我们引入了时间编码，将观察时间戳嵌入了中间特征中。我们的私有数据集和UCR/UEA时间序列档案中的实验表明，我们的模块提高了分类精度，尤其是在部分时间序列获得的短数据上。
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.
