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tus. These sensors generate large amounts of multistream measurements. For instance, the motivating dataset for this research contains system monitoring measurements captured by 61 different sensors located in a paper manufacturing machinery. 2 These raw measurements ought to be processed and analyzed appropriately to obtain useful information regarding the system’s health condition. The general purpose of this article is to develop a practical pipeline to process, analyze, and interpret the system monitoring data given as a form of the multistream time series (MSTS) to detect a system failure that may be occurred in the near future. One challenge problem that we aim to resolve through this project is that the machine failure has to be prognosed ahead of a physical occurrence. The traditional system monitoring tools such as the control chart-based quality control techniques focus on the detection of the assignable causes of the system abnormal status as soon after it occurs as possible. As such, the average run length has been used as the main performance metric for comparing various types of control charts. 3 In the paper manufacturing process, however, once the machine failure occurs, the system instantly stops and there is no benefit to detect the failure afterward. Therefore, it is important to perceive any symptomatic signal followed by a machine breakdown even a few seconds earlier. To achieve this goal, we define our problem as a binary classification task where we aim to distinguish the precursory signs from the normal signals. This problem definition motivated us to use the terminology “MSTS” rather than “multivariate time series” as the multivariate data implies multiple responses in the statistical literature. Other difficulties for this task may be attributed to the multistream nature of the given data and a lack of failure-labeled observations. Although there exist several feature-based classification algorithms for the time series data, it is problematic to generate and select a proper set of features from high dimensional multistream data. As an alternative, the deep learning techniques are emerging as competent tools handling such data. 4,5 However, these techniques require a substantially large amount of data, which is not the case for our problem where the dataset only includes 124 machine breakdown points among more than 18 000 time points where the labeled data points only consist of 0.67% among the whole dataset. Such an extremely imbalanced dataset makes it even harder to build a model with high performance since we don’t have enough labeled data to train the model. To solve the aforementioned problems, we rely on machine learning algorithms, which have been recognized as more powerful techniques for predictive tasks than traditional approaches that do not incorporate these techniques, 5 with properly processed variables and informative features. Specifically, for each sensor or variable, we transform the time series instance into a scalar extracted from its nearest neighbor and feed the transformed variables into a proper off-the-shelf machine learning algorithms to make a classification. The nearest neighbor-based algorithm has been recognized as one of the most effective classification methods for time series data. 6 In this article, we exploit the advantages of 1-nearest neighbor (1-NN) but extend the method for MSTS data. The objectives of these algorithms are to extract suitable features for MSTS classification. First, we extract the class label of the nearest neighbor only considering a single variable, which results in the binary features for each variable. Second, the relative distance to the nearest neighbor is measured, which is anticipated to provide more useful information on an instance’s nearest neighbor. In this research, we demonstrate how to predict the paper machine failure before it occurs (ie, early detec- tion); and find the variables which have a significant effect on causing failures using these nearest neighbor-based features. The rest of this article is organized as follows. Section 2 reviews related work in time series classification. Section 3 shows the overall procedure to implement, and describes dataset, preprocessing, and two versions of algorithms we pro- pose in this article. In Section 4, we evaluate the performance of the proposed algorithms with the real-world dataset of the paper manufacturing sensor signals. Finally, we conclude our research in Section 5. 2 RELATED WORK A wide range of algorithms have been used and proposed to solve classification problems with univariate time series data. Sykacek and Roberts 7 propose an approach with a latent feature representation by applying Bayesian theory to hierarchical time series processing. Esmael et al 8 suggest a hybrid approach to improve the accuracy of the time series classifier with hidden Markov models. Jovic´ et al 9 examine the capability of four common deci- sion tree ensembles in the biomedical time-series dataset. Eads et al 10 employee a support vector machine (SVM) for time series classification with features extracted from the time series data. Cui et al 11 demonstrate convolutional neural networks for time series classification problem to incorporate feature extraction and classification in a sin-
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