Industrial Time Series Prediction

Abstract

Time series prediction is a significant way for forecasting the variables involved in industrial process, which usually identifies the latent rules hidden behind the time series data of the variables by means of auto-regression. In this chapter we introduce the phase space reconstruction technique, which aims to construct the training dataset for modeling, and then a series of data-driven machine learning methods are provided for time series prediction, where some well-known artificial neural networks (ANNs) models are introduced, and a dual estimation-based echo state network (ESN) model is particularly proposed to simultaneously estimate the uncertainties of the output weights and the internal states by using a nonlinear Kalman-filter and a linear one for noisy industrial time series. In addition, the kernel based methods, including Gaussian processes (GP) model and support vector machine (SVM) model, are also presented in this chapter. Specifically, an improved GP-based ESN model is proposed for time series prediction, in which the output weights in ESN modeled by using GP avoids the ill-conditioned phenomenon associated with the generic ESN version. A number of case studies related to industrial energy system are provided to validate the performance of these methods.