Abstract In real-life data of various domains, such as traffic, meteorology, or healthcare data, events may have varying durations. Moreover, heterogeneous multivariate temporal data may consist of varying samplings, including regular sampling in different frequencies or irregular, as well as events data of different types, having fixed or varying duration. We propose to uniformly represent heterogeneous multivariate temporal data using symbolic time-intervals, from which a model that predicts an occurrence of events early can be learned. We introduce a novel use of time-interval-related patterns (TIRPs), in which patterns that end with an event of interest can be used to continuously estimate the event’s occurrence probability in real-time. Recently, we introduced a model that allows continuous prediction of the completion of a pattern, which is extended in this work, to also predict the expected completion time. This work focuses on predicting the probability and time occurrence of an event based on multiple different instances of patterns that end with the event, for which we propose and evaluate aggregation functions. A rigorous evaluation was conducted on four real-life datasets to assess the effectiveness of the proposed model and the aggregation functions. The proposed model performed better than the baseline models (ResNet, LSTM-FCN, ROCKET, and XGBoost) for all datasets.

Predicting an event of interest based on heterogeneous multivariate temporal data is challenging but desirable as it allows the utilization of all types of temporal variables. In various domains, symbolic time intervals (STIs) can be used to represent real-life events that vary in duration, such as the period a traffic light remains green, or the time a patient undergoes treatment or is on medication. Further, heterogeneous multivariate temporal data may be composed of STIs along with event-driven or continuous temporal variables, such as traffic collisions or blood test values. Temporal abstraction can be used to uniformly represent heterogeneous multivariate temporal variables with STIs, from which frequent time intervals related patterns (TIRPs) can be discovered. We extend earlier work on continuous completion prediction of a single TIRP that ends with an event of interest, introducing a continuous prediction method based on multiple different instances of multiple TIRPs that end with the event of interest, for which we propose and evaluate several weighted aggregation functions. The proposed method overall performed better on real-life, medical, and non-medical datasets, than the use of a single TIRP, and in comparison to the baseline models (XGBoost, ResNet, LSTM-FCN, and ROCKET).

Empirical evaluations of visual encodings for analytical tasks inform the design of automatic presentation systems. This study compares anomaly detection effectiveness, efficiency, and user satisfaction using tabular visualization and graphical representation of position, size, and color saturation visualizations and the visualizations’ relative ranking. In our user study analysts used the visualizations to detect anomalies in bivariate quantitative, ordinal, and nominal real data, before and after training. Consistent with Mackinlay ranking of the visual encodings’ effectiveness, the results showed that for all data types, position visualization outperformed size and color saturation visualizations on all measures, before and after training. The use of position visualization after training was as effective as using the table visualization for all data types but significantly easier to use and preferable. Furthermore, the average anomaly detection time was at least three times shorter when using position visualization compared to table visualization for ordinal and quantitative data.

Many time series classification algorithms have been proposed, including deep neural networks based, which so far focused mainly on improving model architectures rather than on data pre-processing. Generalization is crucial in time series classification and it can be achieved by abstracting the data. Data abstraction may also be useful to avoid handling challenges with error measurements, missing values, and irregular sampling. We propose transforming the raw time series into a symbolic time series representation, using a method known as temporal abstraction, before feeding it to the deep neural networks. This transformation can greatly enhance generalization and may potentially improve classification performance. In particular, we investigate the effectiveness of temporal abstraction when combined with convolution-based sequence models or recurrent neural networks. The methods were evaluated on 128 univariate datasets. Our evaluation shows that even when using equal frequency discretization, a relatively simple method, outperforms most state-of-the-art deep neural networks’ performance for univariate time series classification when fed by raw time series.