A novel Domain Adaptive Deep Recurrent Network for multivariate time series prediction

Multivariate time series prediction has attracted growing interest in many research fields. Recently, deep learning has been applied to multivariate time series prediction and has achieved encouraging results. However, in real-world scenarios, the insufficient data of multivariate time series at the beginning of the observation causes the deep learning model unable to exert its expected performance. Furthermore, there is the distribution discrepancy between different multivariate time series caused by many factors, making it unfeasible to reuse existing data or models directly. Therefore, a novel Domain Adaptive Deep Recurrent Network (DADRN) is proposed for multivariate time series prediction with insufficient data, which transferring the knowledge of the target-related time series (source domain) to the target time series (target domain) by minimizing distribution mismatch in the feature sharing space. The DADRN automatically learns the temporal dependence of predictive time series and the dynamic dependencies between multiple time variables through the deep recurrent neural network. Besides, a special transfer learning method, domain adaptation, is embedded in the constructed deep recurrent network to reduce the distribution discrepancy between different domains. The proposed domain independence strategy and domain weighted loss further enhance the DADRN's transfer learning capability by improving the distribution estimation of the target domain and balancing the network's learning on two domains. The reasonable combination of deep recurrent network and domain adaptation endows DADRN with favorable transfer learning capability, and its effectiveness is demonstrated by the experimental results on two real-world datasets.