  Cyber-physical systems often consist of entities that interact with each
other over time. Meanwhile, as part of the continued digitization of industrial
processes, various sensor technologies are deployed that enable us to record
time-varying attributes (a.k.a., time series) of such entities, thus producing
correlated time series. To enable accurate forecasting on such correlated time
series, this paper proposes two models that combine convolutional neural
networks (CNNs) and recurrent neural networks (RNNs). The first model employs a
CNN on each individual time series, combines the convoluted features, and then
applies an RNN on top of the convoluted features in the end to enable
forecasting. The second model adds additional auto-encoders into the individual
CNNs, making the second model a multi-task learning model, which provides
accurate and robust forecasting. Experiments on two real-world correlated time
series data set suggest that the proposed two models are effective and
outperform baselines in most settings.
  This report extends the paper "Correlated Time Series Forecasting using
Multi-Task Deep Neural Networks," to appear in ACM CIKM 2018, by providing
additional experimental results.
