Time series prediction algorithms are widely used for applications such as demand forecasting, weather forecasting and many others to make well informed decisions. In this paper, we compare the most prevalent of these methods as well as suggest our own, where the time series are generated from highly complex industrial processes. These time series are non-stationary and the relationships between the various time series vary with time. Given a set of time series from an industrial process, the challenge is to keep predicting a chosen one as far ahead as possible, with the knowledge of the other time series at those instants in time. This scenario occurs, since the chosen time series is usually very expensive to measure or extremely difficult to obtain compared to the rest. Our studies on real data suggest, that our method is substantially more robust to predicting multiple steps ahead than the existing methods in these complex domains. © 2010 IEEE.