## 1 Introduction  
In the early days of aluminium electrolysis there was no automatic feed control. The operators had to wait until an anode effect (AE) occured, which is always manifested by a rapid increase of pot voltage. Right at the time of occurence they fed the pot with a batch of alumina and waited for the next AE to come. If it came too early the amount of alumina was increased and vice versa. The termination of the AE was established manually with iron rakes or green wood poles. Nowadays there is consens that AEs are detrimental and must be avoided [1; 2; 3]. Because of the high voltage in the range from 10 to 80 V AEs reduce the energy and current efficiency, induce meltback of the side-ledge and can lead to localized sludge. But furthermore AEs cause emissions of harmful perfluorocarbon (PFC) greenhouse gases, CF\({}_{4}\) and C\({}_{2}\)F\({}_{6}\) which have extremely high global warming potentials [2; 3; 4; 5]. Due to the availability of automatic feeders and computer controlled feeding but also automatic AE termination routines the aluminium industry was able to eminently reduce the frequency and duration of AEs [1].  
In the last decades there was further improvement and research--[PERSON] and [PERSON] [1] present different methods for predicting approaching AEs (hysteresis in volt-amp curve, rate of voltage rise, measuring high frequency electrical noise, measuring acoustic noise and pilot anodes) and reduce AE frequency (good condition of pot's lining and electrical connections, high quality alumina and properly maintained feeders, uniform anode current density and a proper ledge).  
[PERSON] [6] suggests an improved maintenance, more focus on pot tending to reduce AE frequency and a stepwise downward shift of the anode beam with specific delays between separate downward movements and an adjusted feeding strategy during termination to reduce the duration of AEs. [PERSON] [7] et al. found that mainly a proper bath height control can reduce AE frequency. [PERSON] et al. [5] analysed the root causes of AEs and categorised them into three groups: Pot controllers feed strategy, maintenance practices and alumina quality. With the introduction of individual anode current measurement (ACM) new methods and models for predicting AEs were proposed [8; 9; 10; 11]. Since this paper deals with data of pots where no ACM is implemented, thus it is based on classic process data, these papers are not discussed in detail. Regarding prediction models using classic process data, [PERSON] et al. [12] and [PERSON] et al. developed [13] univariate threshold-based models. The time difference between the timestamp of detection by the model and the timestamp of detection by the process control system, so called lead time was in the range of seconds. [PERSON] [14] used wavelet packet transform to analyse the pot resistance for AE prediction. [PERSON] [15] applied multivariate statistical techniques in order to predict AEs. [PERSON] et al. [16] developed a machine learning model with a lead time of 30 min and an F\({}_{1}\) score of 99.8%. [PERSON] et al. [9] used a collaborative two-dimensional forecast model with a lead time of 20 min to 40 min and an accuracy of 95%.  
In summary, it can be stated that some predictions methods were only proposed but never applied to a real test dataset of sufficient size or used in a live test scenario. Moreover many authors do not provide enough evaluation criteria like confusion matrix and F\({}_{1}\) score. In this paper a classification model based on aggregated time series data is proposed. Preprocessing, training and testing steps including evaluation criteria are presented with high degree of transparency.