Time series(TS) forecasting has been widely applied in many fields and industrial soft sensor is one of them. Most time series modeling methods require that all inputs are sampled at equal intervals. However, in industry, the process variables are often sampled on different time scales with varying intervals. To address this problem, in this paper, we designed a framework using deep learning with time representation techniques to model the long temporal industrial data with multiple sampling frequency. Data are aggregated into different time scale by time representation and the network extracts the information on both temporal and spatial dimensions simultaneously using the bottleneck layer and one-dimensional filter. The proposed model has a significant improvement compared with other methods and has been deployed in the factory and updated every month.

时间序列（TS）预测已广泛应用于许多领域，工业软传感器就是其中之一。大多数时间序列建模方法要求以相等的间隔对所有输入进行采样。然而，在工业中，过程变量通常在不同的时间尺度上以不同的间隔进行采样。为了解决这个问题，在本文中，我们设计了一个框架，使用深度学习和时间表示技术来对具有多个采样频率的长时态工业数据进行建模。数据通过时间表示聚合到不同的时间尺度，网络使用瓶颈层和一维过滤器同时提取时间和空间维度的信息。