Time series prediction method based on E-CRBM

Abstract

To solve the problems of delayed prediction results and large prediction errors in one-dimensional time series prediction, a time series prediction method based on Error-Continuous Restricted Boltzmann Machines (E-CRBM) is proposed in this paper. This method constructs a deep conversion prediction framework, which is composed of two E-CRBMs and a neural network (NN). Firstly, the E-CRBM models of the original input sequence and the target prediction sequence are trained, respectively, to extract the time features of the two sequences. Then the NN model is used to connect and transform the time features. Secondly, the feature sequence H1 is extracted from the original input sequence of test data through E-CRBM1, which is used as input of NN to obtain feature transformation sequence H2. Finally, the target prediction sequence is obtained by reverse re-construction of feature transformation sequence H2 through E-CRBM2. The E-CRBM in this paper introduces the residual sequence of NN feature transformation in the hidden layer of CRBM, which increases the robustness of CRBM and improves the overall prediction accuracy. The classical time series data (sunspot time series) and the actual operation data of reciprocating compressor are selected in the experiment. Compared with the traditional time series prediction method, the results verify the effectiveness of the proposed method in single-step prediction and multi-step prediction.