## 3 Anode Effect Prediction
### Data Selection and Preprocessing  
Usually in machine learning the whole dataset is splitted into at least training and test data. In this paper the dataset consists of 63% training data (regarding frequency of AEs) and 37% test data both including all 360 pots from all potrooms. It was decided to differ from the well-known 80/20 splitting to have more data for testing, at least half a month. The splitting is shown in the timeline in Figure 2. It is confirmed that in both time intervals the process and the technical state was not altered. It was decided to use a feature based approach to build the prediction model. Because measured data in aluminium electrolysis process is mainly time series data, it is standing to reason to use time series features. Time series features, also called time series characteristics and especially the usage for classification have gained more attention in the last years. [PERSON] et al. [19] analysed over 9000 time series analysis algorithms across an interdisciplinary literature, while [PERSON] et al. [20] reduced this huge amount to a fixed set of only 22 canonical characteristics, called catch22 used for classification. [PERSON] et al. [21] provide a set of 18 features, called tsfeatures. Over the years this set has been continuously extended. In this paper a python implementation of tsfeatures by [PERSON] et al. [22] is used. In general time series characteristics can be seen as a toolbox to solve time series related problems for many domain applications. These characteristics provide a representation of the time series, covering measurements of basic statistics, linear correlations, stationarity, entropy, methods from the physical nonlinear time-series analysis literature, linear and nonlinear model parameters, fits, and predictive power [20]. [PERSON] et al. [20] show the usability for time series with a fixed length. In contrast in this paper the time series do not have a fixed length because the process is running 24 h per day. For this reason a rolling window approach is used, where the window length and the step  
Figure 1: Example of voltage behaviour during an anode effect (AE) with four restarts from train dataset. Figure 2: Splitting of train and test dataset. size are parameters. This rolling window approach applies to the feature generation using catch22 and tsfeatures but also to other features described in the following paragraphs. In the first step of feature generation the pot resistance is chosen. Because of the current modulation in potroom 1, the voltage would strongly depend on a change in current, while the resistance is more independent. It is measured in steps of 3 s, or respectively 0.33 Hz measuring frequency. [PERSON] et al. [19] state that a time series of less than 1000 samples is short, while [PERSON] et al. [20] consider a time series of 500 samples to be short. In the AE prediction context on condition of a low measuring frequency it is concluded, that there is a conflict between a sufficient time series length and an observation time that is still in the near of the AE. To cover both, a rolling window with a length of 10 min (200 samples) and a step size of 1 min is used to get information about the short-term behaviour. On this rolling window catch22 and tsfeatures time series characteristics are applied. Since 200 samples should be considered as a very short time series, not every feature may yield useful results. To get information about the long-term behaviour a second rolling window with a length of 60 min (1200 samples) is used. The step size is limited to 5 min to reduce the calculation time. Some characteristics produced errors during computation, yielded too many special-valued outputs or needed a long calculation time in comparison to others and have therefore been removed. The full list of time series characteristics can be seen in Table 1. Furthermore in a resampling step of 1 min the mean and standard deviation were calculated. After these steps the result is a dataset in steps of 1 min.