Deep Neural Network Architectures for Momentary Forecasting in Dry Bulk Markets: Robustness to the Impact of COVID-19

The COVID-19 pandemic has severely affected various global markets, increasing the need for new forecasting models for the dry bulk market. Therefore, this study proposes deep neural network (abbreviated DNN) architectures to build a model for momentary forecasting that does not affect accuracy in the case of economic shocks (i.e., COVID-19) and elucidates the strategy for obtaining DNNs. First, since momentary and short-term forecastings are fundamentally different, they might use independent methods;as such, I apply DNN for the time series classification to momentary forecasting. Second, the proposed architecture is constructed by considering sparsity, because designing DNN architectures robust to any impacts is a type of overfitting prevention for deep neural networks. Finally, this study proposes indices for quantitatively evaluating the DNN architectures that represent the realized forecasting performance of various deep neural networks. Using these indices, I demonstrate that optimal architectures may need to have model sparsity in the DNN (i.e., sparsity independent of the input data). The importance of this issue has been demonstrated experimentally. As a result, the architectures achieved target performances of 88%, 91%, and 79% accuracy and had stability for Panamax, Supramax, and Capesize vessels, respectively from February 2016 to September 2021 (i.e., five years and eight months). It is difficult to identify a correlation between model performance and volatility. Furthermore, before and after the COVID-19 shock, the performance of the proposed models compared to the optimal one exceeds that of other four recent models, namely 'Facebook Prophet,' 'DARTS,' 'SKTIME,' and 'AutoTS'. © 2013 IEEE.

Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records.

BACKGROUND: COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. METHODS: We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clínicas (São Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient's outcome. RESULTS: Time series-based machine learning models are capable of predicting a COVID-19 patient's outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). CONCLUSIONS: Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.

Singular Spectrum Analysis of Tremorograms for Human Neuromotor Reaction Estimation

Singular spectrum analysis (SSA) is a method of time series analysis and is used in various fields, including medicine. A tremorogram is a biological signal that allows evaluation of a person’s neuromotor reactions in order to infer the state of the motor parts of the central nervous system (CNS). A tremorogram has a complex structure, and its analysis requires the use of advanced methods of signal processing and intelligent analysis. The paper’s novelty lies in the application of the SSA method to extract diagnostically significant features from tremorograms with subsequent evaluation of the state of the motor parts of the CNS. The article presents the application of a method of singular spectrum decomposition, comparison of known variants of classification, and grouping of principal components for determining the components of the tremorogram corresponding to the trend, periodic components, and noise. After analyzing the results of the SSA of tremorograms, we proposed a new algorithm of grouping based on the analysis of singular values of the trajectory matrix. An example of applying the SSA method to the analysis of tremorograms is shown. Comparison of known clustering methods and the proposed algorithm showed that there is a reasonable correspondence between the proposed algorithm and the traditional methods of classification and pairing in the set of periodic components.

Automatic Data Imputation in Time Series Processing Using Neural Networks for Industry and Medical Datasets

Time series classification and regression techniques help solve problems in many knowledge areas, including medicine, electronics, industry, and even music. When we apply them to real-life issues, a common obstacle is the lack of data in intervals within a time series. Usually, to solve it, the missing data is populated with information highly dependent on available datasets, which requires prior analysis. This paper addresses the problem in a novel way, automatically filling the missing data using a mixture of techniques and letting the prediction model decide which filling is better. We tested our approach for classification in industrial and medical datasets and for regression, we used a dataset containing COVID-19 information. Our results are very competitive, and our approach improves the state-of-the-art models. We obtain better performance in all the experiments for the selected quality measures. Most importantly, the improvement is more statistically significant when the amount of missing data is higher. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis.

Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.