###### Abstract  
The following work presents algorithms for semi-automatic validation, feature extraction and ranking of time series measurements acquired from MOX gas sensors. Semi-automatic measurement validation is accomplished by extending established curve similarity algorithms with a slope-based signature calculation. Furthermore, a feature-based ranking metric is introduced. It allows for individual prioritization of each feature and can be used to find the best performing sensors regarding multiple research questions. Finally, the functionality of the algorithms, as well as the developed software suite, are demonstrated with an exemplary scenario, illustrating how to find the most power-efficient MOX gas sensor in a data set collected during an extensive screening consisting of 16,320 measurements, all taken with different sensors at various temperatures and analytes.  
time series analysis; MOX gas sensors; slope based signature; automatic measurement validation; prioritiable ranking; feature extraction +
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for sensor screenings is only half way to the goal. Due to the large amount of raw data captured during an automated screening with many parameter combinations, a manual interpretation can also be very time-consuming and will therefore benefit greatly from a high degree of automation itself.  
There are two main challenges identified for the automatized processing and analysis of the data acquired during an automated screening that have to be addressed algorithmically:  
* Validation Since manufacturing and operating parameters are still under research, some sensors may show a malformed or no response at all. The occurrence of such invalid measurements in a screening is therefore very likely. These measurements need to be sorted out, to only include measurements from proper sensors for the final assessment.
* Ranking To identify the best sensor for a given application, a performance metric is required. It should be based on quantifiable and individually prioritize features extracted from the time series measurements. The ability to tune the metric through feature-wise prioritization will help to model the scenario, for which the ranking is performed, in greater detail.  
In the following work, we will present method combinations and algorithms needed to address the identified challenges. To find invalid or unusual measurements, we propose an automatic validation method based on curve similarity that compares new measurements against a well-known reference to determine how correlative they are. An algorithm calculates a numeric similarity value between the given reference and the curves under test. A threshold can then be used to automatically sort out measurements that are too dissimilar from the reference. Furthermore, we propose a signature extraction algorithm that significantly enhances the performance of the well-established curve metrics, directly improving the numeric similarity results. The solution for the sensor ranking is split into feature extraction and the ranking algorithm itself. Sensor-expert interviews led to the identification of several MOX sensor-specific features. A relevant set of features, extractable from the sensors' time series, was mathematically formalized. Finally, we can use the resulting feature vectors as input for our proposed ranking algorithm, which is based on multiplicative arithmetic. The ranking can individually prioritize a freely selectable combination of features from the vector, to best possibly adapt the ranking process to the target application for the sensor.  
We will conclude by showing the developed algorithms and software on an exemplary ranking performed on a data set obtained during an extensive sensor screening, to automatically find the most sensitive sensor for a given analyte, while consuming as little power for its heater element as possible.