scholarly journals Modelling Covid-19 Pandemic in Nigeria using Multivariate Autoregressive Distributed Lag-Moving Average Models

2021 ◽  
Vol 4 (3) ◽  
pp. 118-134
Author(s):  
Usoro A.E. ◽  
John E.E.
Keyword(s):  
Graphical Representation ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
National Centre ◽  
Distributed Lag Models ◽  
Moving Average Models ◽  
Clinical Measures ◽  
Multivariate Autoregressive

The aim of this paper was to study the trend of COVID-19 cases and fit appropriate multivariate time series models as research to complement the clinical and non-clinical measures against the menace. The cases of COVID-19, as reported by the National Centre for Disease Control (NCDC) on a daily and weekly basis, include Total Cases (TC), New Cases (NC), Active Cases (AC), Discharged Cases (DC) and Total Deaths (TD). The three waves of the COVID-19 pandemic are graphically represented in the various time plots, indicating the peaks as (June–August, 2020), (December–February, 2021), and (July–September, 2021). Multivariate Autoregressive Distributed Lag Models (MARDLM) and Multivariate Autoregressive Distributed Lag Moving Average (MARDL-MA) models have been found to be suitable for fitting different categories of the COVID-19 pandemic in Nigeria. The graphical representation and estimates have shown a gradual decline in the reported cases after the peak in September 2021. So far, the introduction of vaccines and non-pharmaceutical measures by relevant organisations are yielding plausible results, as evident in the recent decrease in New Cases, Active Cases and an increasing number of Discharged Cases, with fewer deaths. This paper advocates consistency in all clinical and non-clinical measures as a way towards the extinction of the dreaded COVID-19 pandemic in Nigeria and the world.

Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting

2020 ◽  
Vol 30 (08) ◽  
pp. 2050039 ◽  
Author(s):  
Foued Saâdaoui ◽  
Othman Ben Messaoud
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Multivariate Time Series ◽  
Seasonal Effects ◽  
Real World Data ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Mode Decomposition ◽  
Nonlinear Trends

Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.

scholarly journals Modelling New Zealand Road Deaths

2021 ◽  
Vol 32 (2) ◽  
pp. 4-15
Author(s):  
Colin Morrison ◽  
Ernest Albuquerque
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Time Data ◽  
Baseline Model ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

New Zealand is developing an integrated road safety intervention logic model. This paper describes a core component of this wider strategic research carried out in 2018: a baseline model that extrapolates New Zealand road deaths to 2025. The baseline will provide context to what Waka Kotahi NZ Transport Agency is trying to achieve. It offers a way of understanding what impact interventions have in acting with and against external influences affecting road deaths and serious trauma. The baseline model considers autonomous change at a macro level given social and economic factors that influence road deaths. Identifying and testing relationships and modelling these explanatory variables clarifies the effect of interventions. Time-series forecasting begins by carefully collecting and rigorously analysing sequences of discrete-time data, then developing an appropriate model to describe the inherent structure of the series. Successful time-series forecasting depends on fitting an appropriate model to the underlying time-series. Several time-series models were investigated in understanding road deaths in the New Zealand context. In the final modelling an autoregressive integrated moving average (ARIMA) model and two differing autoregressive distributed lag (ARDL) models were developed. A preferred model was identified. This ARDL model was used to project road deaths to 2025.

scholarly journals Forecasting Covid-19 Infections and Deaths Horizon in Egypt

2020 ◽  
Author(s):  
Shereen Nosier ◽  
Reham Salah Beram
Keyword(s):  
World War Ii ◽  
Global Economy ◽  
Prediction Models ◽  
Moving Average ◽  
Health Crisis ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
The World ◽  
Total Death ◽  
Egyptian Government

The coronavirus Covid-19 pandemic is defining a global health crisis, which is the hugest challenge the world has faced since World War II. Accordingly, the global economy as well is facing the worst economic catastrophe since the 1930s Great Depression. The case in Egypt is similar to the rest of the world. Despite being threatened by GDP decline and income losses; the Egyptian government has reacted early to restrain the pandemic outbreak. By mid-March, many measures had been undertaken to contain the spread of the virus. More than three months after imposing them, Egypt began lifting many of the restrictions put in place to curb the spread of coronavirus. Predictions of the potential spread of Covid-19 based on time series Auto Regressive Integrated Moving Average (ARIMA) and econometric Autoregressive-Distributed Lag (ARDL) forecasting models are utilized in this paper for designing and/or evaluating countermeasures. The aim of this study is threefold, first using the most recent available data to find the best prediction models for daily cases and death in Egypt and forecast them up to 7 November 2020. Second, to analyze the effect of mobility on the incidence of the pandemic using Google Community Mobility Reports (GCMR) to evaluate the results of easing lockdown restrictions. Finally, providing some recommendations that may help lessen the spread of the virus and eradicate new deaths as possible. The results revealed that mobility of population is affecting the incidence of new cases of Covid-19 significantly over the period of the study. Additionally, the total number of infections on November 7 2020 is expected to reach 102,352 cases, while the total death toll is predicted to be 5,938 according to the most accurate methods of forecasting. Accordingly, in order to sustain the predicted flat pandemic curve, many restrictions must be continued and emergency mechanisms need to be considered. For instance, adhering to the precautions of social distancing advised by the health minister and the declared hygiene rules to ensure that infection is prevented or transmitted is necessary. Besides, being prepared with re-imposing lockdown strategies and health system support are essential among others. It should also be noted that this expected pattern can shift, yet that depends on people's actions.

ASYMPTOTIC BEHAVIOR OF THE CUSUM OF SQUARES TEST UNDER STOCHASTIC AND DETERMINISTIC TIME TRENDS

2011 ◽  
Vol 27 (4) ◽  
pp. 913-927 ◽  
Author(s):  
Bent Nielsen ◽  
Jouni S. Sohkanen
Keyword(s):  
Asymptotic Behavior ◽  
Least Squares ◽  
Time Trends ◽  
Asymptotic Theory ◽  
Ordinary Least Squares ◽  
Nuisance Parameters ◽  
Forecast Errors ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Distributed Lag Models

We generalize the cumulative sum of squares (CUSQ) test to the case of nonstationary autoregressive distributed lag models with deterministic time trends. The test may be implemented with either ordinary least squares residuals or standardized forecast errors. In explosive cases the asymptotic theory applies more generally for the least squares residuals-based test. Preliminary simulations of the tests suggest a very modest difference between the tests and a very modest variation with nuisance parameters. This supports the use of the tests in explorative analysis.

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