scholarly journals Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

2022 ◽  
Vol 19 (2) ◽  
pp. 738
Author(s):  
Abdelgader Alamrouni ◽  
Fidan Aslanova ◽  
Sagiru Mati ◽  
Hamza Sabo Maccido ◽  
Afaf. A. Jibril ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Short Term Memory ◽  
Least Squares Method ◽  
Arima Model ◽  
Information Criterion ◽  
Generalized Least Squares ◽  
Performance Criteria ◽  
Regional Modeling ◽  
Estimation Model

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

scholarly journals Integration of Functional Link Neural Networks into a Parameter Estimation Methodology

2021 ◽  
Vol 11 (19) ◽  
pp. 9178
Author(s):  
Tuan-Ho Le ◽  
Mengyuan Tang ◽  
Jun Hyuk Jang ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Neural Networks ◽  
Robust Design ◽  
Least Squares Method ◽  
Estimation Method ◽  
Information Criterion ◽  
Primary Objective ◽  
Estimation Methods ◽  
Estimation Model ◽  
Functional Link ◽  
Dual Response

In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria.

COVID-19 ARIMA Model Forecast: Confirmed, Mortality & Recovered Cases in 13 Caribbean Countries (Preprint)

2020 ◽  
Author(s):  
Debjyoti Talukdar ◽  
Dr. Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Local Health ◽  
Health Administration ◽  
Model Forecast ◽  
Time Period ◽  
The Impact

BACKGROUND Rapid spread of SARS nCoV-2 virus in Caribbean region has prompted heightened surveillance with more than 350,000 COVID-19 confirmed cases in 13 Caribbean countries namely Antigua and Barbados, Bahamas, Barbados, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago. OBJECTIVE The aim of our study is to analyze the impact of coronavirus (SARS nCoV-2) in 13 Caribbean countries in terms of confirmed cases, number of deaths and recovered cases. Current and projected forecasts using advanced autoregressive integrated moving average (ARIMA) models will enable local health organisations to plan future courses of action in terms of lockdown and managing essential public services. METHODS The study uses the auto regressive integrated moving average (ARIMA) model based upon time series pattern as per data retrieved from John Hopkins University, freely accessible on public domain and used for research and academic purposes. The data was analyzed using STATA 14 SE software between the time period - Jan 22, 2020 till May 27, 2020 using ARIMA time series analysis. It involves generalizing an autoregressive moving average model to better understand the data and predict future points in the time series until June 15, 2020. RESULTS The results show the predicted trend in terms of COVID-19 confirmed, mortality and recovered cases for 13 Caribbean countries. The projected ARIMA model forecast for the time period - May 25, 2020 to May 31, 2020 show 20278 (95% CI 19433.21 - 21123.08) confirmed cases, 631 (95% CI 615.90 - 646.51) deaths and 11501 (95% CI 10912.45 - 12089) recovered cases related to SARS nCoV-2 virus. The final ARIMA model chosen for confirmed COVID-19 cases, number of deaths and recovered cases are ARIMA (4,2,2), ARIMA (2,1,2) and ARIMA (4,1,2) respectively. All chosen models were compared with other models in terms of various factors like AIC/BIC (Akaike Information Criterion/Bayesian Information Criterion), log likelihood, p-value significance, coefficient < 1 and 5% significance. The autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs were plotted to reduce bias and select the best fitting model. CONCLUSIONS As per the results of the forecasted COVID-19 models, there is a steady rise in terms of confirmed, recovered and mortality cases during the time period March 1, 2020 until May 27, 2020. It shows an increasing trend for confirmed and recovered COVID-19 cases and slowing of the number of mortality cases over a period of time. The predicted model will help the local health administration to devise public policies in terms of awareness measures, lockdown and essential health services accordingly.

Water Use Trend at Universiti Teknologi Malaysia: Application of ARIMA Model

2012 ◽  
Author(s):  
Ayob Katimon ◽  
Amat Sairin Demun
Keyword(s):  
Time Series ◽  
Water Use ◽  
Autocorrelation Function ◽  
Water Consumption ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
University Campus ◽  
Autoregressive Integrated Moving Average ◽  
Partial Autocorrelation Function

Kertas kerja ini menerangkan aplikasi kaedah permodelan (ARIMA) bagi mewakili perilaku penggunaan air di kampus Universiti Teknologi Malaysia. Menggunakan fungsi–fungsi ACF, PACF dan AIC, siri masa penggunaan air bulanan di kampus UTM boleh dinyatakan dalam model ARIMA (2,0,0). Anggaran parameter model ø1 dan ø2 ialah 0.2747 dan 0.4194. Keadaan tersebut menggambarkan bahawa penggunaan air pada bulan semasa tidak semestinya dipengaruhi dengan tepat oleh kadar penggunaan air pada bulan sebelumnya. Analisis juga menunjukkan model ARIMA (2,0,0) boleh diguna sebagai model ramalan guna air di kampus universiti. Kata kunci: Guna air, kampus universiti, siri masa, model ARIMA The paper describes the application of autoregressive integrated moving average (ARIMA) model to represent water use behaviour at Universiti Teknologi Malaysia (UTM) campus. Using autocorrelation function (ACF), partial autocorrelation function (PACF), and Akaike’s Information Criterion (AIC), monthly campus water use series can be best presented using ARIMA (2,0,0) model. The estimated parameter of the model ø1 and ø2 are 0.2747 and 0.4194 respectively. This implies that water consumption in UTM campus at the present month is not necessarily influenced by water consumption of immediate previous month. Analysis shows that ARIMA (2,0,0) model provides a reasonable forecasting tool for campus water use. Key words: Water use, university campus, time series, ARIMA model

scholarly journals Predicting Economic Performance of Bangladesh using Autoregressive Integrated Moving Average (ARIMA) model.

2021 ◽  
pp. 129-148
Author(s):  
Raad Mozib Lalon ◽  
Nusrat Jahan
Keyword(s):  
Autocorrelation Function ◽  
Economic Performance ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Unit Root Test ◽  
Information Criterion ◽  
Gdp Growth ◽  
Autoregressive Integrated Moving Average ◽  
Q Model

This paper attempts to forecast the economic performance of Bangladesh measured with annual GDP data using an Autoregressive Integrated Moving Average (ARIMA) Model followed by test of goodness of fit using AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) index value among six ARIMA models along with several diagnostic tests such as plotting ACF (Autocorrelation Function), PACF (Partial Autocorrelation Function) and performing Unit Root Test of the Residuals estimated by the selected forecasting ARIMA model. We have found the appropriate ARIMA (1,0,1) model useful in predicting the GDP growth of Bangladesh for next couple of years adopting Box-Jenkins approach to construct the ARIMA (p,r,q) model using the GDP data of Bangladesh provided in the World Bank Data stream from 1961 to 2019. JEL classification numbers: B22, B23, C53. Keywords: GDP growth, ACF, PACF, Stationary, ARIMA (p,r,q) model, Forecasting.

scholarly journals Prediction of COVID-19 Confirmed Cases after Vaccination: Based on Statistical and Deep Learning Models

2021 ◽  
Vol 3 (2) ◽  
pp. 153-165
Author(s):  
Meejoung Kim
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Statistical Models ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
A Cell ◽  
Partial Autocorrelation Function ◽  
Autoregressive Conditional Heteroscedasticity

In this paper, we analyze and predict the number of daily confirmed cases of coronavirus (COVID-19) based on two statistical models and a deep learning (DL) model; the autoregressive integrated moving average (ARIMA), the generalized autoregressive conditional heteroscedasticity (GARCH), and the stacked long short-term memory deep neural network (LSTM DNN). We find the orders of the statistical models by the autocorrelation function and the partial autocorrelation function, and the hyperparameters of the DL model, such as the numbers of LSTM cells and blocks of a cell, by the exhaustive search. Ten datasets are used in the experiment; nine countries and the world datasets, from Dec. 31, 2019, to Feb. 22, 2021, provided by the WHO. We investigate the effects of data size and vaccination on performance. Numerical results show that performance depends on the used data's dates and vaccination. It also shows that the prediction by the LSTM DNN is better than those of the two statistical models. Based on the experimental results, the percentage improvements of LSTM DNN are up to 88.54% (86.63%) and 90.15% (87.74%) compared to ARIMA and GARCH, respectively, in mean absolute error (root mean squared error). While the performances of ARIMA and GARCH are varying according to the datasets. The obtained results may provide a criterion for the performance ranges and prediction accuracy of the COVID-19 daily confirmed cases.Doi: 10.28991/SciMedJ-2021-0302-7 Full Text: PDF

scholarly journals Stochastic ARIMA model for annual rainfall and maximum temperature forecasting over Tordzie watershed in Ghana

2018 ◽  
Vol 37 (1) ◽  
pp. 127-140 ◽  
Author(s):  
Mexoese Nyatuame ◽  
Sampson K. Agodzo
Keyword(s):  
Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Complex Nature ◽  
Mean Square ◽  
Rainfall Prediction

AbstractThe forecast of rainfall and temperature is a difficult task due to their variability in time and space and also the inability to access all the parameters influencing rainfall of a region or locality. Their forecast is of relevance to agriculture and watershed management, which significantly contribute to the economy. Rainfall prediction requires mathematical modelling and simulation because of its extremely irregular and complex nature. Autoregressive integrated moving average (ARIMA) model was used to analyse annual rainfall and maximum temperature over Tordzie watershed and the forecast. Autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to identify the models by aid of visual inspection. Stationarity tests were conducted using the augmented Dickey–Fuller (ADF), Mann–Kendall (MK) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests respectively. The chosen models were evaluated and validated using the Akaike information criterion corrected (AICC) and also Schwartz Bayesian criteria (SBC). The diagnostic analysis of the models comprised of the independence, normality, homoscedascity, P–P and Q–Q plots of the residuals respectively. The best ARIMA model for rainfall for Kpetoe and Tordzinu were (3, 0, 3) and (3, 1, 3) with AICC values of 190.07 and 178.23. That of maximum temperature for Kpetoe and Tordzinu were (3, 1, 3) and (3, 1, 3) and the corresponding AICC values of 23.81 and 36.10. The models efficiency was checked using sum of square error (SSE), mean square error (MSE), mean absolute percent error (MAPE) and root mean square error (RMSE) respectively. The results of the various analysis indicated that the models were adequate and can aid future water planning projections.

scholarly journals Development of LSTM&CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks

2020 ◽  
Author(s):  
Caglar Uyulan
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Language Processing ◽  
Roc Curve ◽  
Short Term Memory ◽  
Confusion Matrix ◽  
Learning Model ◽  
Classification Model ◽  
Performance Criteria ◽  
Deep Learning Model

AbstractRecent studies underline the contribution of brain-computer interface (BCI) applications to the enhancement process of the life quality of physically impaired subjects. In this context, to design an effective stroke rehabilitation or assistance system, the classification of motor imagery (MI) tasks are performed through deep learning (DL) algorithms. Although the utilization of DL in the BCI field remains relatively premature as compared to the fields related to natural language processing, object detection, etc., DL has proven its effectiveness in carrying out this task. In this paper, a hybrid method, which fuses the one-dimensional convolutional neural network (1D CNN) with the long short-term memory (LSTM), was performed for classifying four different MI tasks, i.e. left hand, right hand, tongue, and feet movements. The time representation of MI tasks is extracted through the hybrid deep learning model training after principal component analysis (PCA)-based artefact removal process. The performance criteria given in the BCI Competition IV dataset A are estimated. 10-folded Cross-validation (CV) results show that the proposed method outperforms in classifying electroencephalogram (EEG)-electrooculogram (EOG) combined motor imagery tasks compared to the state of art methods and is robust against data variations. The CNN-LSTM classification model reached 95.62 % (±1.2290742) accuracy and 0.9462 (±0.01216265) kappa value for datasets with four MI-based class validated using 10-fold CV. Also, the receiver operator characteristic (ROC) curve, the area under the ROC curve (AUC) score, and confusion matrix are evaluated for further interpretations.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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