Classical Prediction Methods Research Articles

Multiview feature augmented neural network for knowledge graph embedding

Link prediction in knowledge graph embedding is a meaningful research topic. Knowledge graph embedding (KGE) focuses on the problem of predicting missing links based on triples. Neural networks have been the paradigm of choice in knowledge graph tasks. However, the general network KGE models lack attention to the spatial location connection between entities and relations and have a weakness in that they cannot capture global information. We found that multiview feature construction can obtain more feature information corresponding to entities and relations. Aggregation of multiple types of multiview spatial transform information is a critical issue. Therefore, we propose a knowledge graph embedding method called the multiview feature augmented neural network (MFAE), which involves three components: multiview spatial transform, feature fusion convolution and feature information augmentation. To precisely augment the fusion of the vector spatial transform, a feature augmented convolutional network with attentive information calculation is introduced as a triple prediction. The multiview spatial transform is combined with a feature augmented convolutional network, which captures global feature information, obtains multiple views of entity and relation information, and improves the effectiveness of KGE. We conduct extensive experiments on link prediction, the effect of different views and feature augmented neural network comparison on benchmark datasets such as FB15k-237 and WN18RR. Experiments show that MFAE delivers significant performance compared to the classical link prediction methods.

Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data

Short-term estimation and prediction of pedestrian density in urban hot spots (e.g., railway station, shopping mall, etc.) is an important topic for traffic management and control in densely populated areas. In this paper, we propose a short-term pedestrian density estimation and prediction method based on mobile phone data. Firstly, pedestrian density in hot spots is estimated using mobile phone data. To decrease the positioning errors of mobile phone data, a modified particle filter method, which considers the movements of pedestrians, is applied for pre-processing the data. An efficient spatial access method (i.e., Hilbert R-tree) is adopted to construct pedestrians’ position indexes for realizing the short-term estimation. Secondly, based on the estimation results, the spatiotemporal extended Kalman filter (SEKF) is proposed for the short-term prediction of pedestrian density. A massive mobile phone dataset collected in Nanjing, China is used in the case study. The estimated pedestrian density from Monday to Thursday is used for pedestrian density prediction on Friday. The results show that the proposed method can estimate and predict pedestrian density in hot spots, especially in small-scale sites of hot spots efficiently in a short time. Comparing with classical prediction methods, the proposed SEKF method predicts short-term pedestrian density in urban hot spots more accurately.

Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk.

Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small–cell lung cancer, and LUAD, respectively.

Measurement Error Assessment for Smart Electricity Meters Under Extreme Natural Environmental Stresses

The measurement error assessment for smart electricity meters consists of the measurement error prediction and the stress factors evaluation, which can be used for improving equipment quality and saving power grid costs, especially under extreme natural environmental stresses. However, actual measurement error assessment suffers from the environmental noise and insufficient feature information. To tackle this problem, in this article, an optimized kernel density estimation (OKDE) is first proposed to identify potential outliers, where a modified distance function and adaptive kernel bandwidth are used to obtain the outlier score. Next, a measurement error assessment method, namely the modified double-kernel support vector regression (MKSVR), is proposed to fuse measurement error and multiple stress features using the modified double-kernel function. Combining the OKDE and MKSVR, actual dataset from the high dry heat region shows that the proposed assessment framework has better evaluation performance. Compared with some classical prediction methods, the OKDE–MKSVR framework has profound outlier detection and measurement error assessment performance under the small sample conditions.

Design and development of novel LiCl–NaCl–KCl–ZnCl2 eutectic chlorides for thermal storage fluids in concentrating solar power (CSP) applications

Molten chloride mixtures are of great scientific and technical interest for sensible heat storage in solar power generation. In this work, the condensed phases in a LiCl–NaCl–KCl–ZnCl 2 multicomponent system were critically evaluated and predicted via literature review and simulations of LiCl–KCl and KCl–ZnCl 2 systems. Two innovative LiCl–NaCl–KCl–ZnCl 2 quaternary chloride systems with low melting points were proposed and developed on the basis of a thermodynamic design. The thermal–physical properties of these two eutectic compositions, including melting point, mixing enthalpy, specific heat capacity, density, thermal conductivity, and mass loss, were then experimentally determined. The experimental results were consistent with the theoretical predictions. The novel quaternary eutectic chlorides with excellent thermophysical properties developed herein are potential candidates for thermal energy storage and transfer materials in concentrating solar power plants. • Thermodynamic prediction of LiCl–NaCl–KCl–ZnCl 2 system was performed via CALPHAD. • Two novel quaternary eutectic chlorides were successfully designed for thermal energy storage and transfer. • Thermophysical properties of the system were determined by experiment and classical prediction method. • Thermodynamic performance of the novel chlorides was substantially improved compared with other potential materials. • The novel quaternary eutectic chlorides were proposed as potential candidate materials in CSP applications.

A temporal fusion transformer for short-term freeway traffic speed multistep prediction

Accurate short-term freeway speed prediction is a key component for intelligent transportation management and can help travelers plan travel routes. However, very few existing studies focus on predicting one-hour ahead or longer freeway speed. In this study, a novel architecture called Temporal Fusion Transformer (TFT) is adopted to predict freeway speed with the prediction horizons from 5 min to 150 min. The TFT can capture short-term and long-term temporal dependence by a multi-head attention mechanism. Moreover, the TFT utilizes the fusion decoder to import various types of inputs which can improve the prediction accuracy. To demonstrate the advantage of the TFT, traffic speed data collected from an interstate freeway in Minnesota are used to train and test the prediction model. The TFT prediction performance is compared with several classic traffic prediction methods, and the results reveal that the TFT performs best in speed prediction when the prediction horizon is longer than 30 min.

Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks

Predicting taxi-calling demands at the urban area level is vital to coordinate the supply–demand balance of the urban taxi system. Differing travel patterns, the impact of external data, and the expression of dynamic spatiotemporal demand dependence pose challenges to predicting demand. Here, a framework using residual attention graph convolutional long short-term memory networks (RAGCN-LSTMs) is proposed to predict taxi-calling demands. It consists of a spatial dependence (SD) extractor, which extracts SD features; an external dependence extractor, which extracts traffic environment-related features; a pattern dependence (PD) extractor, which extracts the PD of demands for different zones; and a temporal dependence extractor and predictor, which leverages the abovementioned features into an LSTM model to extract temporal dependence and predict demands. Experiments were conducted on taxi-calling records of Shanghai City. The results showed that the prediction accuracies of the RAGCN-LSTMs model were a mean absolute error of 0.8664, a root mean square error of 1.4965, and a symmetric mean absolute percentage error of 43.11%. It outperformed both classical time-series prediction methods and other deep learning models. Further, to illustrate the advantages of the proposed model, we investigated its predicting performance in various demand densities in multiple urban areas and proved its robustness and superiority.

Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion.

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

MNERLP-MUL: Merged node and edge relevance based link prediction in multiplex networks

In multiplex networks, nodes can have multiple types of relationships (links) encoded into different layers such that each layer represents a single type of link. Even though the nature of links in different layers may differ, the nodes themselves remain the same, and so do their underlying relations among themselves. Combining the information in all the layers into one single network such that link prediction can be performed using all the available information is an ongoing research problem. In this work, we theorize that to accurately perform this link prediction, we have to take into account the relevance of both the edges as well as the nodes that connect two directly unconnected nodes. First, we utilize an aggregation model that encodes the information from different layers into one summarized weighted static network, taking into account the relative density of the layers themselves. Then, we propose an algorithm, MNERLP−MUL, which first calculates node and edge relevance based on the summarized graph, and then we combine both these factors to perform link prediction on unconnected pairs of nodes. The edge relevance is calculated using the information from the immediate vicinity of the edge (local information), while node relevance is calculated based on the node’s importance to the overall structure of the graph (global information). We use this methodology to model our method on quasi-local link prediction approaches, which attempt to inculcate properties of both local and global properties for increased accuracy. We compare our method with classical link prediction methods for weighted graphs, and the results indicate its superior performance, both on the summarized weighted graph and original layers.

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion.

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.

Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model.

In recent years, machine learning models facilitated notable performance improvement in landslide displacement prediction. However, most existing prediction models which ignore landslide data at each time can provide a different value and meaning. To analyze and predict landslide displacement better, we propose a dynamic landslide displacement prediction model based on time series analysis and a double-bidirectional long short term memory (Double-BiLSTM) model. First, the cumulative landslide displacement is decomposed into trend and periodic displacement components according to time series analysis via the exponentially weighted moving average (EWMA) method. We consider that trend displacement is mainly influenced by landslide factors, and we apply a BiLSTM model to predict landslide trend displacement. This paper analyzes the internal relationship between rainfall, reservoir level and landslide periodic displacement. We adopt the maximum information coefficient (MIC) method to calculate the correlation between influencing factors and periodic displacement. We employ the BiLSTM model for periodic displacement prediction. Finally, the model is validated against data pertaining to the Baishuihe landslide in the Three Gorges, China. The experimental results and evaluation indicators demonstrate that this method achieves a better prediction performance than the classical prediction methods, and landslide displacement can be effectively predicted.

Analysis of roll center compensation method for underwater gliders based on deep learning

Underwater glider (UG) is one of the most promising low-power and long-voyage autonomous ocean observation platforms. During the gliding, the roll regulation unit (RRU) frequently works to regulate the disturbed heading due to the influence of time-varying current, biofouling, and cumulative error of RRU. In this paper, an accurate energy consumption model of RRU (ERRU) is established to quantify the impact of RRU on the overall energy consumption, and the trial data collected by the Petrel-L glider, China, in the South China Sea are used to verify the accuracy of ERRU. To minimize the working frequency of RRU, a new roll center compensation method (RCCM) is proposed based on variational mode decomposition and long short-term memory (VMD-LSTM). This study analyzes six classical prediction methods based on deep learning, and the VMD-LSTM at k = 5 method is the optimal one to predict the deviation of roll center for UGs. The results indicate that the proposed RCCM can obtain accurate and reliable compensation value for the roll center and effectively restrain the working frequency of RRU. Compared with the data before compensation, the ERRU of the Petrel-L glider is reduced by approximately 22%. Furthermore, the RCCM can apply to other similar UGs.

Three-Dimensional Seismic-Wave Propagation Simulations in the Southern Korean Peninsula Using Pseudodynamic Rupture Models

ABSTRACTAccurate and practical ground-motion predictions for potential large earthquakes are crucial for seismic hazard analysis of areas with insufficient instrumental data. Studies on historical earthquake records of the Korean Peninsula suggest that damaging earthquakes are possible in the southeastern region. Yet classical ground-motion prediction methods are limited in considering the physical rupture process and its effects on ground motion in complex velocity structures. In this study, we performed ground-motion simulations based on rigorous physics through pseudodynamic source modeling and wave propagation simulations in a 3D seismic velocity model. Ensembles of earthquake scenarios were generated by emulating the one- and two-point statistics of earthquake source parameters derived from a series of dynamic rupture models. The synthetic seismograms and the distributions of simulated peak ground velocities (PGVs) were compared with the observations of the 2016 Mw 5.4 Gyeongju earthquake in the Korean Peninsula. The effects of surface-wave radiation, rupture directivity, and both local and regional amplifications from the 3D wave propagation were reproduced accurately in the spatial distribution of simulated PGVs, in agreement with the observations from dense seismic networks by mean log residuals of −0.28 and standard deviations of 0.78. Amplifications in ground motions were found in regions having low crustal velocities and in regions of constructive interference from the crustal shear-wave phases associated with postcritical reflections from the Moho discontinuity. We extended the established approach to earthquake scenarios of Mw 6.0, 6.5, and 7.0, at the same location, to provide the distribution of ground motions from potential large earthquakes in the area. Although we demonstrate the value of these simulations, improvements in the accuracy of the 3D seismic velocity model and the scaling relationship of the source models would be necessary for a more accurate estimation of near-source ground motions.

Diffusion coefficients at infinite dilution of carbon dioxide and methane in water, ethanol, cyclohexane, toluene, methanol, and acetone: A PFG-NMR and MD simulation study

Diffusion coefficients at infinite dilution are important basic data for all processes involving mass transfer. They can be obtained from studying samples in equilibrium using nuclear magnetic resonance spectroscopy with pulsed field gradients (PFG-NMR), a technique which is widely used in chemistry but is only rarely applied in engineering studies. This advantageous technique was employed here to measure the self-diffusion coefficients of diluted solutions of carbon dioxide and methane in the pure solvents water, ethanol, cyclohexane, toluene, methanol, and acetone at 298.15 K. For the systems (carbon dioxide + water) and (carbon dioxide + ethanol), measurements were also carried out at 308.15 K, 318.15 K and 333.15 K. Except for (methane + water) and (methane + toluene), no literature data for the methane-containing systems were previously available. At the studied solute concentrations, there is practically no difference between the self-diffusion coefficient and the mutual diffusion coefficient. The experimental results are compared to experimental literature data as well as to results from semi-empirical methods for the prediction of diffusion coefficients at infinite dilution. Furthermore, molecular dynamics simulations were carried out for all systems to determine the diffusion coefficient at infinite dilution based on force fields that were taken from the literature, and the results are compared to the experimental data and those from the classical prediction methods.

LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification

BackgroundLong noncoding RNAs (lncRNAs) have dense linkages with various biological processes. Identifying interacting lncRNA-protein pairs contributes to understand the functions and mechanisms of lncRNAs. Wet experiments are costly and time-consuming. Most computational methods failed to observe the imbalanced characterize of lncRNA-protein interaction (LPI) data. More importantly, they were measured based on a unique dataset, which produced the prediction bias.ResultsIn this study, we develop an Ensemble framework (LPI-EnEDT) with Extra tree and Decision Tree classifiers to implement imbalanced LPI data classification. First, five LPI datasets are arranged. Second, lncRNAs and proteins are separately characterized based on Pyfeat and BioTriangle and concatenated as a vector to represent each lncRNA-protein pair. Finally, an ensemble framework with Extra tree and decision tree classifiers is developed to classify unlabeled lncRNA-protein pairs. The comparative experiments demonstrate that LPI-EnEDT outperforms four classical LPI prediction methods (LPI-BLS, LPI-CatBoost, LPI-SKF, and PLIPCOM) under cross validations on lncRNAs, proteins, and LPIs. The average AUC values on the five datasets are 0.8480, 0,7078, and 0.9066 under the three cross validations, respectively. The average AUPRs are 0.8175, 0.7265, and 0.8882, respectively. Case analyses suggest that there are underlying associations between HOTTIP and Q9Y6M1, NRON and Q15717.ConclusionsFusing diverse biological features of lncRNAs and proteins and exploiting an ensemble learning model with Extra tree and decision tree classifiers, this work focus on imbalanced LPI data classification as well as interaction information inference for a new lncRNA (or protein).

On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures

In the last years, web surveys have established themselves as one of the main methods in empirical research. However, the effect of coverage and selection bias in such surveys has undercut their utility for statistical inference in finite populations. To compensate for these biases, researchers have employed a variety of statistical techniques to adjust nonprobability samples so that they more closely match the population. In this study, we test the potential of the XGBoost algorithm in the most important methods for estimation that integrate data from a probability survey and a nonprobability survey. At the same time, a comparison is made of the effectiveness of these methods for the elimination of biases. The results show that the four proposed estimators based on gradient boosting frameworks can improve survey representativity with respect to other classic prediction methods. The proposed methodology is also used to analyze a real nonprobability survey sample on the social effects of COVID-19.

Proteomics-Enabled Deep Learning Machine Algorithms Can Enhance Prediction of Mortality

BackgroundIndividualized risk prediction represents a prerequisite for providing personalized medicine. ObjectivesThis study compared proteomics-enabled machine-learning (ML) algorithms with classical and clinical risk prediction methods for all-cause mortality in cohorts of patients with cardiovascular risk factors in the LIFE-Heart Study, followed by validation in the PLIC (Progressione della Lesione Intimale Carotidea) study. MethodsUsing the OLINK-Cardiovascular-II panel, 92 proteins were measured in a cohort of 1,998 individuals from the LIFE-Heart Study (derivation) and 772 subjects from the PLIC cohort (external validation). We constructed protein-based mortality prediction models using eXtreme Gradient Boosting (XGBoost) and a neural network, comparing the prediction performance with classical clinical risk scores (Systemic Coronary Risk Evaluation, Framingham), logistic and Cox regression models. ResultsAll-cause mortality occurred in 156 (8%) patients in the internal validation and 68 (9%) patients in the external validation cohort, within a median follow-up of 10 and 11 years, respectively. On internal and external validation, the Framingham Risk Score achieved areas under the curve (AUCs) of 0.64 (95% CI: 0.59-0.68) and 0.65 (95% CI: 0.58-0.74), logistic regression AUCs of 0.65 (95% CI: 0.57-0.73) and 0.67 (95% CI: 0.59-0.74), Cox regression AUCs of 0.55 (95% CI: 0.51–0.59) and 0.65 (95% CI: 0.57-0.73), the XGBoost classifier AUCs of 0.83 (95% CI: 0.79-0.87) and 0.91 (95% CI: 0.86-0.95), the XGBoost survival estimator AUCs of 0.83 (95% CI: 0.79-0.87) and 0.93 (95% CI: 0.88-0.97), and the neural network AUCs of 0.87 (95% CI: 0.83-0.91) and 0.94 (95% CI: 0.90-0.98), respectively (modern vs classical ML: P < 0.001). ConclusionsML-driven multiprotein risk models outperform classical regression models and clinical scores for prediction of all-cause mortality in patients at increased cardiovascular risk.

Predicting lncRNA-Protein Interaction With Weighted Graph-Regularized Matrix Factorization.

Long non-coding RNAs (lncRNAs) are widely concerned because of their close associations with many key biological activities. Though precise functions of most lncRNAs are unknown, research works show that lncRNAs usually exert biological function by interacting with the corresponding proteins. The experimental validation of interactions between lncRNAs and proteins is costly and time-consuming. In this study, we developed a weighted graph-regularized matrix factorization (LPI-WGRMF) method to find unobserved lncRNA–protein interactions (LPIs) based on lncRNA similarity matrix, protein similarity matrix, and known LPIs. We compared our proposed LPI-WGRMF method with five classical LPI prediction methods, that is, LPBNI, LPI-IBNRA, LPIHN, RWR, and collaborative filtering (CF). The results demonstrate that the LPI-WGRMF method can produce high-accuracy performance, obtaining an AUC score of 0.9012 and AUPR of 0.7324. The case study showed that SFPQ, SNHG3, and PRPF31 may associate with Q9NUL5, Q9NUL5, and Q9UKV8 with the highest linking probabilities and need to further experimental validation.

Towards link inference attack against network structure perturbation

The increasing popularity and diversity of social media sites have resulted in an emergent number of available social networks. These social networks are now the source of information for third-party consumers, such as researchers and advertisers, to understand user social activities. In a privacy-preserving viewpoint, a full assessment of social relationships between individuals may violate privacy. Different network structure perturbation methods have been proposed to limit the disclosure of sensitive user data. However, despite the proliferation of these methods, currently, there are no robustness studies on the methods for link prediction-based hidden inference structure. In this study, we survey the state-of-the-art network structure perturbation methods for privacy-preservation and the classic link prediction methods for structure inference. To restore the perturbed network structure effectively, we propose a novel Multi-Layer Linear Coding-based link prediction method (MLLC) with a closed-form solution. Furthermore, we provide vulnerability analysis on network structure perturbation methods in the context of link prediction-based structure inference. We also compare the methods on the preservation of utility metrics for social network analysis, where a structure perturbation method is preferred if the metrics of the perturbed network are similar to those of the original network. Our experimental study indicates that the MLLC algorithm outperforms conventional methods for hidden structure inference, and that it is important to provide robustness to network structure perturbation methods against these attacks.

Using Artificial Neural Network Model to Prediction the Number of Peoples Afflicted by the Epidemic of (COVID-19) in Iraq

Time-series prediction is an important statistical topic to help researchers in planning and making the right decisions, so this study deals with modern prediction methods, represented by the Artificial Neural Network models, specifically the multi-layered neural network, and the back propagation algorithm has been relied upon several times for training and less selection. A value for error to obtain the best model for describing the data, as well as classic prediction methods such as Box- Jenkins' models, the model was applied to real data represented by the number of people infected with Coronavirus (Covid-19) in Iraq for the period from 2/24/2020 until 3/5/ 2020 On a daily basis, the results showed that future predictions for the number of people infected with Coronavirus began to decline and then stabilized in the period (30-67). The data were analyzed and the results were extracted depending on the statistical program R.