Non-time Series Data Research Articles

A study of online academic risk prediction based on neural network multivariate time series features

SummaryNeural networks are becoming increasingly widely used in various fields, especially for academic risk forecasts. Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms and deep learning algorithms to achieve academic risk prediction has garnered increased attention and development. However, most of these studies use nontime series data as features for prediction, which are slightly insufficient in terms of timeliness. Therefore, this article focuses on time series data features that are more expressive of changes in students' learning status and uses multivariate time series data as predictive features. This article proposes a method based on multivariate time series features and a neural network to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third, the new feature is converted into a final prediction result using a linear classification model. Comparative tests show that the proposed method has high effectiveness.

An intelligent adverse delivery outcomes prediction model based on the fusion of multiple obstetric clinical data

Adverse delivery outcomes is a major re-productive health problem that affects the physical and mental health of pregnant women. Obviously, obstetric clinical data has periodically time series characteristics. This paper proposed a three stage adverse delivery outcomes prediction model via the fusion of multiple time series clinical data. The first stage is data aggregation, in which the data set is collected from the obstetric clinical data and divided based on time series features. In the second stage, a multi-channel gated cycle unit is used to solve the calculation error caused by irregular sampling of time series data. The hidden layer feature vector is connected with the fully connected layer, reshaped into a new one-dimensional feature, and fused with the non-time series data into a new data set. The third stage is the prediction stage of adverse delivery outcomes. By connecting the multi-channel gated cycle unit with the extreme gradient lift, the data transmitted in the corresponding channel is used in the feature extraction stage, in which the weighted entropy-based feature extraction is adopted. With the help of the extracted features, a hybrid artificial neural network architecture (MGRU-XGB) was developed to predict adverse delivery outcomes. The experimental results showed that the hybrid model had the best prediction performance for adverse delivery outcomes compared with other single models in terms of sensitivity, specificity, AUC and other evaluation indexes.

Classification with 2-D convolutional neural networks for breast cancer diagnosis

Breast cancer is the most common cancer in women. Classification of cancer/non-cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state-of-the-art classification model—convolutional neural network (CNN), however, cannot be used with such kind of tabular clinical data that are represented in 1-D format. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors—apart from the time series data—cannot be used with CNN, but with other classification models such as Recurrent Neural Networks for tabular data or Random Forest. We have proposed three novel preprocessing methods of data wrangling that transform a 1-D data vector, to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non-image tabular data to image data transformation for the non-time series data. The transformed data processed with CNN using VGGnet-16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset.

Integrating Machine Learning Algorithms With Quantum Annealing Solvers for Online Fraud Detection

Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems can only detect the incidents after they have already occurred, not at or near real time. As spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection even more challenging. This study has proposed a detection framework and implemented it using Support Vector Machine (SVM) enhanced with quantum annealing solvers. To evaluate its detection performance and examine the impact of feature selection, we have applied this quantum machine learning (QML) system along with systems built with twelve traditional machine learning methods on two datasets: Israel credit card transactions (non-time series) which is moderately imbalanced and a bank loan dataset (time series) that is highly imbalanced. The result shows that, the quantum enhanced SVM has categorically outperformed the rest in both speed and accuracy with the bank loan dataset. However, it’s detection accuracy is similar to others with Israel credit card transactions data. Furthermore, for both datasets, feature selection has been shown to significantly improve the detection speed, although the improvement on accuracy is marginal. These findings have demonstrated the potential of QML applications on time series based, highly imbalanced data, and the merit of traditional machine learning approaches in non-time series data. This study provides insight on selecting appropriate approach with different types of datasets while taking the tradeoffs of speed, accuracy, and cost into consideration.

Exploring the Spatial-Temporal Characteristics of Traditional Public Bicycle Use in Yancheng, China: A Perspective of Time Series Cluster of Stations

Traditional dock-based public bicycle systems continue to dominate cycling in most cities, even though bicycle-sharing services are an increasingly popular means of transportation in many of China’s large cities. A few studies investigated the traditional public bicycle systems in small and mid-sized cities in China. The time series clustering method’s advantages for analyzing sequential data used in many transportation-related studies are restricted to time series data, thereby limiting applications to transportation planning. This study explores the characteristics of a typical third-tier city’s public bicycle system (where there is no bicycle-sharing service) using station classification via the time series cluster algorithm and bicycle use data. A dynamic time warping distance-based k-medoids method classifies public bicycle stations by using one-month bicycle use data. The method is further extended to non-time series data after format conversion. The paper identified three clusters of stations and analyzed the relationships between clusters’ features and the stations’ urban environments. Based on points-of-interest data, the classification results were validated using the enrichment factor and the proportional factor. The method developed in this paper can apply to other transportation analysis and the results also yielded relevant strategies for transportation development and planning.

Machine Learning Interpretability Techniques for Outage Prediction: A Comparative Study

Interpretable machine learning has recently attracted a lot of interest in the community. Currently, it mainly focuses on models trained on non-time series data. LIME and SHAP are well-known examples and provide visual-explanations of feature contributions to model decisions on an instance basis. Other post-hoc approaches, such as attribute-wise interpretations, also focus on tabular data only. Little research has been done so far on the interpretability of predictive models trained on time series data. Therefore, this work focuses on explaining decisions made by black-box models such as Deep Neural Networks trained on sensor data. In this paper, we present the results of a qualitative study, in which we systematically compare the types of explanations and the properties (e.g., method, computational complexity) of existing interpretability approaches for models trained on the PHM08-CMAPSS dataset. We compare shallow models such as regression trees (with limited depth) and black-box models such as Long-Short Term Memories (LSTMs) and Support Vector Regression (SVR). We train models on processed sensor data and explain their output using LIME, SHAP, and attribute-wise methods. Throughout our experiments, we point out the advantages and disadvantages of using these approaches for interpreting models trained on time series data. Our investigation results can serve as a guideline for selecting a suitable explainability method for black-box predictive models trained on time-series data.

3DACN: 3D Augmented convolutional network for time series data

Time series data and non-time series data are increasing in the credit system of financial market, so that an effective and intelligent data mining model plays a critical role to analyze hybrid time series data. In addition, traditional mining models sometimes fail to converge because of imbalanced data problem. Therefore, we propose a 3D Augmented Convolutional Network (3DACN) to extract time series information and solve the serious imbalanced data problem. By using the augmented algorithm on time series data, hybrid time series data are enlarged to generate more examples on the minority classes. 3DACN ensures the latent variables with an Expectation-Maximization(EM) algorithm to improve F1 score (F1) and Area Under Curve (AUC). Experimental results show that in the benchmark of Bank database, it can gain F1 score by 81.1% and the AUC by 88.2% respectively; while in the benchmark of Credit Risk database, the 3DACN can reach high performance on F1 score by 88.1% and the AUC by 88.4%.

Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data

This paper addresses the problem of missing latent time series information caused by the differences in the analysis of time series data and non-time series data. A time series trend structure model (TSTM) was established using the analysis of time series patterns and rules, the trends of patterns and rules, and trends in confidence and support. Shandong Province was selected as the study area. Rainfall and evaporation time series data from this area were input into the TSTM. The results show that: (1) the structure of multi-year precipitation and evaporation trends of the meteorological stations in the study area have continuously increasing or decreasing characteristics. The TSTM can excavate the different trend structure characteristics of different meteorological elements and enables diversity in time series data analysis; (2) the evaporation trend structure tends to change synchronously with increases and decreases in precipitation and evaporation. The synchronous change frequency is essentially the same as that of the rainfall trend structure. This indicates that the TSTM has spatial and temporal characteristics for time series data analysis; and (3) from the maximal non-descending and non-ascending subsequence in the TSTM, it can be concluded that there exists continuity in the years when the trend structure of precipitation and evaporation increases and decreases synchronously. In addition, the degree of similarity in the model is well reflected in the spatial distribution characteristics of time series data, and the model provides clustering characteristics for time series data analysis. The TSTM proposed in this paper can effectively obtain the potential hydrological information contained in time series data, and provides a scientific and reliable basis for rules for the spatial optimization of watershed data and for the calibration of hydrological models.

MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework.

As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data. Thus, in this paper, a deep learning-based python package for data integration is developed. The python package deep learning-based multimodal longitudinal data integration framework (MildInt) provides the preconstructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved. We validated the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer’s disease to predict the disease progression. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset.

Dynamic full Bayesian ensemble classifiers for small time series

Improving the reliability of small time series classifiers with continuous attributes is an important and challenging task. The information contained in small time series is not sufficient and a temporal dependency exists between data records, which makes it very difficult to optimize the fitting degree between the classifier and the data, and many mature techniques of non-time series data classifiers are not practical. We use a dynamic full Bayesian classifier to increase the amount of information provided by the attribute to the class, and realize the fusion of temporal and nonsequential information. By combining the conditional joint density estimation of attributes based on the multivariate Gaussian kernel function with a diagonal smoothing parameter matrix, the interval division of smoothing parameter values, the timing progressive classification accuracy criterion, the construction of the smoothing parameter configuration tree, classifier selection and averaging, etc, a dynamic full Bayesian ensemble classifier was established for small time series. Experiments were performed using small time series in macroeconomic analysis. The results show that the optimized dynamic full Bayesian ensemble classifiers have very good classification accuracy.

An Intelligent Quick Prediction Algorithm With Applications in Industrial Control and Loading Problems

The Artificial Neural Network (ANN) and its variations have been well-studied for their applications in the prediction of industrial control and loading problems. Despite showing satisfactory performance in terms of accuracy, the ANN models are notorious for being slow compared to, e.g., the traditional statistical models. This substantially hinders ANN model's real-world applications in control and loading prediction problems. Recently a novel learning approach of ANN called Extreme Learning Machine (ELM) has emerged and it is proven to be very fast compared with the traditional ANN. In this paper, an Intelligent Quick Prediction Algorithm (IQPA), which employs an extended ELM (ELME) in producing fast, stable, and accurate prediction results for control and loading problems, is devised. This algorithm is versatile in which it can be used for short, medium to long-term predictions with both time series and non-time series data. Publicly available power plant operations and aircraft control data are employed for conducting analysis with this proposed novel model. Experimental results show that IQPA is effective and efficient, and can finish the prediction task with accurate results within a prespecified time limit.

Improving cluster-based missing value estimation of DNA microarray data

We present a modification of the weighted K-nearest neighbours imputation method (KNNimpute) for missing values (MVs) estimation in microarray data based on the reuse of estimated data. The method was called iterative KNN imputation (IKNNimpute) as the estimation is performed iteratively using the recently estimated values. The estimation efficiency of IKNNimpute was assessed under different conditions (data type, fraction and structure of missing data) by the normalized root mean squared error (NRMSE) and the correlation coefficients between estimated and true values, and compared with that of other cluster-based estimation methods (KNNimpute and sequential KNN). We further investigated the influence of imputation on the detection of differentially expressed genes using SAM by examining the differentially expressed genes that are lost after MV estimation. The performance measures give consistent results, indicating that the iterative procedure of IKNNimpute can enhance the prediction ability of cluster-based methods in the presence of high missing rates, in non-time series experiments and in data sets comprising both time series and non-time series data, because the information of the genes having MVs is used more efficiently and the iterative procedure allows refining the MV estimates. More importantly, IKNN has a smaller detrimental effect on the detection of differentially expressed genes.