Complex Time Series Analysis Based on Conditional Random Fields

Yanjie Wei, Haifeng Guo, Donghua Yang, Mengmeng Li, Bo Zheng, Hongzhi Wang

Abstract: A fundamental problem with complex time series analysis involves data prediction and repair. However, existing methods are not accurate enough for complex and multidimensional time series data. In this paper, we propose a novel approach, a complex time series prediction model, which is based on the conditional random field (CRF) and recurrent neural network (RNN). This model can be used as an upper-level predictor in the stacking process or be trained using deep learning methods. Our approach is more accurate than existing methods in some suitable scenarios, as shown in the experimental results.