论文标题
丢失传感器数据的顺序到序列插补
Sequence-to-Sequence Imputation of Missing Sensor Data
论文作者
论文摘要
尽管序列到序列（编码器）模型被认为是深度学习序列模型中的最新模型，但对于使用此模型来恢复缺失的传感器数据，几乎没有研究。关键挑战是缺少的传感器数据问题通常包括三个序列（一系列观察到的样品，然后是一系列缺失的样品序列，然后是另一个观察到的样品序列），而序列对序列模型仅考虑两个序列（一个输入序列和输出序列）。我们通过以新颖的方式制定顺序到序列来解决这个问题。正向RNN编码在丢失序列之前观察到的数据，而向后的RNN编码丢失序列后观察到的数据。解码器以一种新颖的方式解码了两个编码器，以预测丢失的数据。我们证明，该模型在12％的病例中产生的错误比当前最新的错误多。
Although the sequence-to-sequence (encoder-decoder) model is considered the state-of-the-art in deep learning sequence models, there is little research into using this model for recovering missing sensor data. The key challenge is that the missing sensor data problem typically comprises three sequences (a sequence of observed samples, followed by a sequence of missing samples, followed by another sequence of observed samples) whereas, the sequence-to-sequence model only considers two sequences (an input sequence and an output sequence). We address this problem by formulating a sequence-to-sequence in a novel way. A forward RNN encodes the data observed before the missing sequence and a backward RNN encodes the data observed after the missing sequence. A decoder decodes the two encoders in a novel way to predict the missing data. We demonstrate that this model produces the lowest errors in 12% more cases than the current state-of-the-art.
