Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria

The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria, using the data on daily confirmed cases. To validate the predictions, these models were compared through data tests. The test results show that the CART regression model outperformed the ARIMA model in terms of accuracy, leading to a fast growth in the number of confirmed COVID-19 cases. The research findings help governments to make proper decisions on how the prepare for the outbreak. Besides, our analysis reveals the lack of quarantine wards in Nigeria, in addition to the insufficiency of medications, medical staff, lockdown decisions, volunteer training, and economic preparation

Forecasting Of Exchange Rate In Regime Switch: Evidence From Non –Linear Time Series Model

Most of macro- economic variables exhibit cyclical behaviors, state dependency and regime switching and modeling such series using univariate linear time series models fails. This study test for non –linearity and linearity in the behavior of the Naira/US dollar exchange rate where the exchange rate exhibits a regime switching between the year 1970 and 1994 using a non- linear time series class model to describe the structure of the series. For this purpose, a logistic smooth transition regression was detected to fit a yearly data over the sample period 1970 to 2013. There is evidence of non-linearity in the behavior of the series and also evidence of continuous of regimes or allow of smoothing the series as it was revealed from the large value of the smoothing parameter. The model estimated was powerful and eligible in the sense of conformity to economic reality and sounds usefulness for decision makers, forecasters to forecast macro-economic variables that exhibit cyclical behavior through the use of this type non – linear class time series model

Modelling and Forecasting Climate Time Series with State-Space Model

This study modelled and estimated climatic data using the state-space model. The study was specifically to identify the pattern of the trend movement i.e., increase or decrease in the occurrence of the climatic change; to use of Univariate Kalman Filter for the computation of the likelihood function for climatic projections; to modelling the climatic dataset using the state-space model and to assess the forecasting power of the state-space models. The data used for the work includes temperature and rainfall for periods January 1991 to December 2017. The data are tested for normality. Shapiro-Wilk, Anderson-Darling and Kolmogorov-Smirnov test of normality for the climatic data all showed that the variables are not normally distributed. The work spans the use of breaking trend regression model to fit climatic data to estimate the slopes which show much increase in climatic data has been recorded from the initial time data collection until the present. Investigations and diagnostic are carried out by checking for corrections in the residuals and also checking for periodicity in the residuals. The results of this investigation show significant autocorrelation in the residuals indicating the presence of underlying noise terms which is not accounted for. By treating the residual as an autoregressive moving average (ARMA) process whereby we can obtain its spectral density, the result from the parametric spectral estimate shows underlying periodic patterns for monthly data, thus, leads to a discussion on the need to treat climatic data as a structural time series model. We select appropriate models by considering the goodness of fit of the model by comparing the Akaike information criterion (AIC) values. Parameters are estimated and accomplished with some measures of precision

Nigerian COVID-19 Incidence Modeling and Forecasting with Univariate Time Series Model

The occurrence of COVID-19 has given rise to dreadful medical difficulties due to its hyper-endemic effects on the human population. This made it fundamental to model and forecast COVID-19 pervasiveness and mortality to control the spread viably. The COVID-19 data used was from February, 28, 2020 to March 1, 2021. ARIMA(1,2,0) was selected for modeling COVID-19 confirmed and ARIMA (1,1,0) for death cases. The model was shown to be adequate for modeling and forecasting Nigerian COVID-19 data based on the ARIMA model building results. The forecasted values from the two models indicated Nigerian COVID-19 cumulative confirmed and death case continues to rise and maybe in-between 189,019–327,426 and interval 406–3043, respectively in the next 3 months (May 30, 2021). The ARIMA models forecast indicated an alarming rise in Nigerian COVID-19 confirmed and death cases on a daily basis. The findings indicated that effective treatment strategies must be put in place, the health sector should be monitored and properly funded. All the protocols and restrictions put in place by the NCDC, Nigeria should be clung to diminish the spread of the pandemic and possible mortality before immunizations that can forestall the infection is developed

Modeling and Forecasting the Third wave of Covid-19 Incidence Rate in Nigeria Using Vector Autoregressive Model Approach

Modeling the onset of a pandemic is important for forming inferences and putting measures in place. In this study, we used the Vector autoregressive model to model and forecast the number of confirmed covid-19 cases and deaths in Nigeria, taking into account the relationship that exists between both multivariate variables. Before using the Vector Autoregressive model, a co-integration test was performed. An autocorrelation test and a heteroscedasticity test were also performed, and it was discovered that there is no autocorrelation at lags 3 and 4, as well as no heteroscedasticity. According to the findings of the study, the number of covid-19 cases and deaths is on the rise. To forecast the number of cases and deaths, a Vector Autoregressive model with lag 4 was used. The projection likewise shows a steady increase in the number of deaths over time, but a minor drop in the number of confirmed Covid-19 cases

A REPARAMETRISED AUTOREGRESSIVE MODEL FOR MODELLING GROSS DOMESTIC PRODUCT

This paper is used to propose a reparametrised autoregressive model that is capable of analyzing time series data that follows a non-Gaussian marginal distribution. The Anderson Darling Statistics was used to identify that Nigerian Gross domestic product followed a Gamma distribution. The proposed Gamma autoregressive (GAR) and classical autoregressive models were fitted using a Maximum Likelihood Estimation (MLE) method. The Akaike Information Criteria (AIC) was used to select AR(2) and GAR(2) as the optimal models but GAR(2) was chosen because it has the least value of AIC. The comparison between AR(2) and GAR(2) models based on the values of Mean absolute error (MAE), Mean absolute prediction error (MAPE) and Root mean square error (RMSE) indicted that GAR(2) will yield a more accurate forecast than AR(2). In essence, GAR model is a viable alternative and better model for analyzing GDP growth rate

Ridge Estimation’s E�ectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations

The goal of this research is to compare multiple linear regression coe�cient estimation technique with multicollinearity. In order to quantify the e�ectiveness of estimations by the mean of average mean square error, the ordinary least squares technique (OLS), modified ridge regression method (MRR), and generalized Liu-Kejian method (LKM) are compared with the Average Mean Square Error (AMSE). For this study, the simulation scenarios are 3 and 5 independent variables with zero mean normally distributed random error of variance 1, 5, and 10, three correlation coe�cient levels; i.e., low (0.2), medium (0.5), and high (0.8) are determined for independent variables, and all combinations are performed with sample sizes 15, 55, and 95 by Monte Carlo simulation technique for 1,000 times in total. As the sample size rises, the AMSE decreased. The MRR and LKM both outperformed the OLS. At random error of variance 10, the MRR is the most suitable for all circumstance

Vector Autoregressive Modeling of COVID-19 Incidence Rate in Nigeria

With the outbreak of COVID-19, a lot of studies have been carried out in various science disciplines to either reduce the spread or control the increasing trend of the disease. Modeling the outbreak of a pandemic is pertinent for inference making and implementation of policies. In this study, we adopted the Vector autoregressive model which takes into account the dependence that exists between both multivariate variables in modeling and forecasting the number of confirmed COVID-19 cases and deaths in Nigeria. A co-integration test was carried out prior to the application of the Vector Autoregressive model. An autocorrelation test and a test for heteroscedasticity were further carried out where it was observed that there exists no autocorrelation at lag 3 and 4 and there exists no heteroscedasticity respectively. It was observed from the study that there is a growing trend in the number of COVID-19 cases and deaths. A Vector Autoregressive model of lag 4 was adopted to make a forecast of the number of cases and death. The forecast also reveals a rising trend in the number of infections and deaths. The government therefore needs to put further measures in place to curtail the spread of the virus and aim towards flattening the curve

On Non-Linear Non-Gaussian Autoregressive Model with Application to Daily Exchange Rate

The most often used distribution in statistical modeling follows Gaussian distribution. But many real-life time series data do not follow normal distribution and assumptions; therefore, inference from such a model could be misleading. Thus, a reparameterized non-Gaussian Autoregressive (NGAR) model that has the capabilities of handling non-Gaussian time series was proposed, while Anderson Darling statistics was used to identify the distribution embedded in the time series. In order to determine the performance of the proposed model, the Nigerian monthly exchange rate (Dollar-Naira Selling Rate) was analyzed using proposed and classical autoregressive models. The proposed model was used to determine the joint distribution of the observed series by separating the marginal distribution from the serial dependence. The maximum Likelihood (MLE) estimation method was used to obtain an optimal solution in estimating the generalized gamma distribution of the proposed model. The selection criteria used in this study were Akaike Information Criterion (AIC). The result revealed through the value of the Anderson Darling statistics that the data set were not normally distributed. The best model was selected using the minimum values of AIC value. The study concluded that the proposed model clearly shows that the non-Gaussian Autoregressive model is a very good alternative for analyzing time series data that deviate from the assumptions of normality and, in particular, for the estimation of its parameters

Knowledge, Attitude, and Perception of Health and Non-Healthcare Workers Towards COVID-19 Vaccination: Machine Learning Approach

There have been concerns globally as to whether taking COVID-19 vaccination is harmful or not. In this study, we conducted an online survey to measure the knowledge and attitude of people, first about COVID-19, and second about COVID-19 vaccination—various analyses such as descriptive statistics, logistic regression, and support vector regression with k-fold cross-validation. The support vector machine and tuned support vector machine suggest a better fit based on cross-validation error. The results show that immigration requirements significantly explain why an individual would accept the COVID-19 vaccine. This study suggests that people in authority should look into people's concerns regarding taking the COVID-19 vaccine and address them accordingly. The study aims to draw the attention of the people to the concern that surrounds taking COVID-19 vaccination and explored various statistical techniques to draw inference