Time series emerge in various applications such as financial data and production data, however, most of the generated data exhibit nonlinear inter-dependency between samples and noise, making necessary the development of methods capable of handling such nonlinearities and other abnormalities. In this paper we present an architecture for prediction of time series embedded in noise. The proposed architecture combines a convolutional and long short term memory (LSTM) layers into a structure similar to an analysis filterbank of two channels. The first element of each channel is a convolutional layer followed by a LSTM, which is able to find temporal dependencies of the signal. Finally the channels are summed to obtain a prediction. We found that the frequency response of the filters resemble a complementary filter bank response, with each channel having a maximum at different bands which could suggest that it characterizes the incoming signal in frequency. Comparisons with other methods demonstrate that the proposed method offer much better results in terms of different error measures.

时间序列出现在各种应用中，例如财务数据和生产数据，但是，大多数生成的数据显示出样本和噪声之间的非线性相互依赖性，因此有必要开发能够处理此类非线性和其他异常情况的方法。在本文中，我们提出了一种用于预测嵌入噪声中的时间序列的体系结构。所提出的体系结构将卷积和长期短期记忆（LSTM）层组合到一个类似于两个通道的分析滤波器组的结构中。每个通道的第一个元素是一个卷积层，后跟一个LSTM，它能够找到信号的时间依赖性。最后，将通道相加以获得预测。我们发现滤波器的频率响应类似于互补的滤波器组响应，每个通道在不同频段上具有最大值，这可能暗示它可以表征输入信号的频率。与其他方法的比较表明，根据不同的误差度量，该方法可提供更好的结果。