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standard deviations from the mean (z-score = 3) was too discriminatory and we settled on z-score = 2 for extracting anomalies in the data. This returned 25 data points in ZDF audience data instead of 4, for example (Fig. 3). Figure 3: Outliers in ZDF audience data with z-score = 2

Figure 3 standard deviations from the mean (z-score = 3) was too discriminatory and we settled on z-score = 2 for extracting anomalies in the data. This returned 25 data points in ZDF audience data instead of 4, for example (Fig. 3). Figure 3: Outliers in ZDF audience data with z-score = 2

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The prediction of future values of some continuous time-series data set is generally referred to as forecasting and has been applied in various domains. Time series are usually decomposed by prediction models like ARIMA into three components: trend, seasonality and remainder (also called ‘irregular’). An example decomposition is shown in Fig. 1. Figure 1: “The electrical equipment orders (top) and its three additive components” (Hyndman and Athanasopoulos, 2018).
The prediction of future values of some continuous time-series data set is generally referred to as forecasting and has been applied in various domains. Time series are usually decomposed by prediction models like ARIMA into three components: trend, seasonality and remainder (also called ‘irregular’). An example decomposition is shown in Fig. 1. Figure 1: “The electrical equipment orders (top) and its three additive components” (Hyndman and Athanasopoulos, 2018).
Table 1: Identification of relationship between Events and “Anomalies” in TV audience data In Table 1, we summarize the results of looking at each anomaly for each channel and manually determining if they relate to (a) a TV specific event (like a series finale), (b) an external event broadcast on that channel (like live sports coverage), or (c) not explained. It can be seen that no anomaly was unexplainable. Only in the PRO7 case the anomalies occurred due to a TV- specific event, in fact they were the weekly broadcasts of “Germany’s Next Top Model” which attracted a much higher audience that any other programming on that channel. The weekly repetition of these outliers could be used to learn that this is related more to the schedule of TV programming than to external events (which do not occur as regularly). For all other channels, we could explain all of the anomalies by events that occurred at that time and were broadcast on that channel, indicating both that outliers in audience data can be meaningful for prediction and that they need identification with events for prediction model learning.
Table 1: Identification of relationship between Events and “Anomalies” in TV audience data In Table 1, we summarize the results of looking at each anomaly for each channel and manually determining if they relate to (a) a TV specific event (like a series finale), (b) an external event broadcast on that channel (like live sports coverage), or (c) not explained. It can be seen that no anomaly was unexplainable. Only in the PRO7 case the anomalies occurred due to a TV- specific event, in fact they were the weekly broadcasts of “Germany’s Next Top Model” which attracted a much higher audience that any other programming on that channel. The weekly repetition of these outliers could be used to learn that this is related more to the schedule of TV programming than to external events (which do not occur as regularly). For all other channels, we could explain all of the anomalies by events that occurred at that time and were broadcast on that channel, indicating both that outliers in audience data can be meaningful for prediction and that they need identification with events for prediction model learning.
Table 2: Prediction model with TV content categorization as additional feature compared to the benchmark
Table 2: Prediction model with TV content categorization as additional feature compared to the benchmark
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