# Information Extraction from Industrial Sensor Data Using Time Series Meta-Features
## 2 Literature Overview  
We reviewed some of the time series meta-features literature and divided it into six domains, i.e., regression, classification, clustering, anomaly detection, visualization, and meta-learning, which are listed in Table 1. [PERSON] et al. (2016) calculated meta-features, i.e., the mean for numeric data and the sum for binary data, of several process variables in a rolling window to predict the bath temperature of reduction cells using a random forest. [PERSON] et al. (2017) used several time series meta-features from ([PERSON] et al., 2018; [PERSON] et al., 2018) in a rolling window to predict anode effects in reduction cells using logistic regression, linear support vector machine, random forest and eXtreme Gradient Boosting. [PERSON] et al. (2018) used a multilayer perceptron (MLP) and eight time series meta-features to classify patterns in control charts. The authors compared the performance of the MLP with another MLP that is solely based on the values of the time series. In further experiments, the performance of both MLPs was analyzed with noise corrupted time series and time series at varying length. [PERSON] et al. (2018) used hierarchical clustering and seven parameters (meta-features) to divide reduction cells with similar behavior into control groups. [PERSON] et al. (2015) computed 18 meta-features from time series representing the performance of servers at the internet company Yahoo. With the help of a principal component analysis (PCA), \(\alpha\)-hulls and highest density regions, the authors used the calculated meta-features to identify entire time series that differed from other time series in  
Figure 2: One of the three pot rooms operated at the Essen site in Germany by TRIMET Aluminium SE. One pot room contains 120 reduction cells in an end-to-end configuration. the data set. In [22], the extreme value theory was used to identify anomalous streaming time series data. The presented framework is based on the calculation of 14 time series meta-features, which are used in an offline and an online phase. The offline phase is used to train a model on the typical behavior of the system. Afterward, the model was deployed in the online phase to identify anomalous time series using a rolling window. Furthermore, the authors present an algorithm that updates the model if a significant change in the distribution of the typical behavior of the system is detected. In a practical example, the authors used the framework to identify anomalous sensor data. [PERSON] et al. [23] computed six time series meta-features to visualize and analyze the M3-Competition data set [24]. The authors performed a PCA using the calculated meta-features, then visualized the first and second principal component (PC) to obtain an overview of the time series characteristics of the M3-Competition data set. Furthermore, the two-dimensional feature space and a genetic algorithm were used to generate new time series that extend the original data set. The authors then compared the feature space with the performance of selected forecasting methods. [PERSON] et al. present in [9] the FFORMS framework which includes the training of a classifier with time series meta-features to predict an algorithm that might be appropriate in forecasting a time series. The authors in [25] applied the meta-learning approach to automate the process of selecting forecasting models for individual time series using meta-features. In their first case study decision trees with 10 meta-features calculated from stationary time series were trained to choose between two forecasting models. The second case study considers NOEMON [26] and five meta-features, which were calculated for each time series in the yearly M3-Competition data set [24], to rank forecasting models, i.e., random walk, [PERSON]'s linear exponential smoothing, and auto-regressive model. The presented literature shows that time series meta-feature are widely used in different domains.