The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

Time-series forecasting (TSF) is a traditional problem in the field of artificial intelligence, and models such as recurrent neural network, long short-term memory, and gate recurrent units have contributed to improving its predictive accuracy. Furthermore, model structures have been proposed to combine time-series decomposition methods such as seasonal-trend decomposition using LOESS. However, this approach is learned in an independent model for each component, and therefore, it cannot learn the relationships between the time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time-series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using four univariate and four multivariate time-series datasets. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results indicate that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.

The event log recorded through an information system may be missing for various reasons, which fact may result in an imperfect event log. Performing analyses using such an imperfect event log can seriously affect the quality of the obtained results. Therefore, analyses should be performed only after processing of the missing part in the imperfect event log. In the fields of data mining and statistical analysis, various methodologies have been developed to handle data with missing values, but there are not many studies dealing with incomplete event logs that have missing data in the field of process mining. In this paper, we propose a likelihood-based Multiple Imputation by Event Chain (MIEC) method for dealing with imperfect event logs with missing data. An experiment was performed using sample event logs, and a case study was conducted using a real steel manufacturing event log to verify our method. We expect the proposed method to repair the imperfect event log to a high level and to obtain analysis result with high quality even if there are many missing data.

Time-series clustering remains a challenge in data mining. Although novel deep-learning-based representation learning integrated with deep clustering methods have considerably enhanced the performance of time-series clustering, efficiently capturing the various temporal patterns inherent in the data is difficult in representation learning for time-series data. In this study, we proposed a novel representation learning method called temporal multi-features representation learning (TMRL) to capture various temporal patterns embedded in time-series data. Based on TMRL, we introduce the temporal multi-features representation clustering (TMRC) framework for performing time-series clustering. The proposed framework decomposes the input time-series data into k temporal patterns and uses k LSTM autoencoders to extract specialized features for each decomposed diverse temporal pattern through TMRL. Variational-mode decomposition is used to extract temporal multi-features. Finally, temporal multi-features derived from TMRL are ensembled for time-series clustering. To evaluate the superiority of the proposed method, comparative experiments were conducted with 36 publicly available time-series datasets against 16 baseline models. In the comparative experiments, we achieved the highest RI and normalized mutual information values in 12 time-series datasets. Particularly, on datasets consisting of eight types of activities and spectral types, the proposed method attained the highest RI and NMI values in six datasets. Furthermore, visualization results of the learned features through TMRL demonstrated superior representation learning compared with existing methods. These results indicated that the proposed TMRC framework is highly suitable for the learning representations of time-series data and can be effectively used for time-series clustering.

In most computing services, imperfect event logs with missing events are generated for a variety of reasons. Because missing events in imperfect event logs adversely affect the results of process mining analysis, it is essential to handle them effectively. Most existing process mining studies focus on methodologies for generation of good process models, very few methodologies, in fact, have been developed to deal with missing events. To the best of our knowledge, there is a lack of high-performance methods for restoration of missing events in actual event log data. In this paper, we propose a new categorical event imputation method that can restore missing categorical events by learning the structural features between observed events in the event log. We evaluated the proposed method by way of comparative experiments with previous studies using six real datasets, and the results demonstrate that the restoration performance was greatly improved and that thereby, our proposed method can significantly improve both the quality of event logs (specifically by restoring missing events in imperfect event logs) and the overall quality of process mining analysis.