## 3 Anode Effect Prediction
### Data Selection and Preprocessing
Using the previously mentioned resistance mean, the percentage change with respect to a rolling median with a length of 60 min was calculated. Additionally the linear trend of the resistance mean with a rolling window of 5 min was calculated. At TAE more process data (5 min steps) is available that has been included in the dataset: Because of the balanced feed strategy there is a process variable called alumina interval, which gives information in percentage about the current feed phase. Since the minimal feed cycle time is 95 min and the underfeed phases are not fixed, the short-term behaviour is covered with a rolling mean with a length of 60 min and the long-term behaviour is covered with a rolling mean with a length \begin{table}
\begin{tabular}{c c} \hline
**Tsfeatures** & **Catch22** \\ \hline acf\_features\_x\_acf1 & DN\_HistogramMode\_5 \\ acf\_features\_x\_acf10 & DN\_HistogramMode\_10 \\ acf\_features\_diff1\_acf1 & CO\_flecac \\ acf\_features\_diff1\_acf10 & CO\_FirstMin\_ac \\ acf\_features\_diff2\_acf1 & CO\_HistogramAMI\_even\_2\_5 \\ acf\_features\_diff2\_acf10 & CO\_trev\_1\_num \\ count\_entropy & MD\_hrv\_classic\_pnn40 \\ crossing\_points & SB\_BinaryStats\_mean\_longstreth\_1 \\ flat\_spots & SB\_TransitionMatrix\_3 ac\_sumdiagcov \\ lumpiness & PD\_PeriodicityWang\_th\_01 \\ nonlinearity & IN\_AutoMutualInfoStats\_40\_gaussian\_fmmi \\ sparsity & FC\_LocalSimple\_mean\_taurestat \\ stability & DN\_OutlierInclude\_p\_001\_mdrmd \\ unitroot\_kpss & DN\_OutlierInclude\_n\_001\_mdrmd \\ unitroot\_pp & SP\_Summaries\_welch\_rect\_area\_5\_1 \\ SB\_BinaryStats\_diff\_longstreth\_0 \\ SB\_MotifThree\_quantile\_hh \\ SC\_FluctAnal\_2\_rsrangeffit\_50\_1\_logi\_prop\_r1 \\ SC\_FluctAnal\_2\_dfa\_50\_1\_2\_logi\_prop\_r1 \\ SP\_Summaries\_welch\_rect\_control \\ FC\_LocalSimple\_mean3\_stderr \\ \hline \end{tabular}
\end{table} 
Table 1: Time series characteristics used for feature generation with a rolling window. of 8 h. On top of that, the difference between the actual resistance and the lower and upper band was calculated. A root cause analysis of the train dataset has shown that about 30% of all AEs have a technical cause which is mostly either feeder- or breaker-related. Regarding breaker as a technical cause, this takes up around 9% of all AEs. This analysis coincides with the findings by [PERSON] et al. [5] for the same polline but other timespan where breakers as a technical cause were in the range of 9 to 14%. For this reason new features were generated counting error messages by the two breakers each with a rolling window of 60 min and 8 h.  
Beside the so called 5 min-data there are manual measurements. Regarding these, only the bath temperature is available on a daily basis. Other measurements are performed less frequently. The univariate distribution of the bath temperature in Figure 3 shows that AEs tend to occur more often at lower bath temperatures. This is because the alumina dissolution is attenuated at lower temperatures [3]. Except for liquidus temperature other manual measurements like for example, bath height, metal height and superheat have distributions of AE and normal state that overlap to a great extent. On this account and the difficulty to impute measurements that are only available every second day or even worse, bath temperature only was merged with the present dataset. An overview of all features that are used as input for the machine learning model can be seen in Figure 4. The bold font denotes list of features. The letter X can be replaced by 1 and 2, because there are two breakers in each pot. All features were merged by time. Figure 3: Distribution of bath temperature in training dataset.