Automatic formation of observation objects for system state assessment from time series

Improving classification quality metrics requires advances not only in data processing models but also in the related processes of dataset formation and training. The implementation of information sequence processing methods depends on data representation and requires an appropriate choice of model and tuning of its hyperparameters. The final result is significantly influenced by the way the original time series are transformed into observation objects, since this transformation determines the properties of the training dataset and the conditions for the subsequent application of classification algorithms. Therefore, the problem of observation object formation can be considered as an independent stage of data preparation affecting the quality of system state assessment. This paper considers the automatic formation of observation objects for system state assessment based on time series and information sequences. Unlike classical approaches, where the model is adapted to the data, the proposed method is based on solving the inverse problem, in which datasets of observation objects are formed from time series and information sequences so that a predefined model achieves the best value of a selected quality metric. Experiments were carried out using both model data and benchmark datasets. By varying the window width and shift, datasets of observation objects with different properties were generated from the time series. The models were trained on these datasets, and the characteristics of the observation objects and datasets yielding the best results were identified. Quality metrics were obtained and analysed. The results show that forming observation objects from sequences with specific window length and shift properties allows improving precision by 3–8 % compared with the average values obtained over the corresponding datasets. The proposed solution can be integrated into the development of new data processing methods and used to enhance existing models. The formation of datasets from observation objects with different properties extends the possibilities of model training and provides an additional mechanism for improving classification quality.