Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

Daniyal, Muhammad; Tawiah, Kassim; Muhammadullah, Sara; Opoku-Ameyaw, Kwaku

doi:10.1155/2022/4802743

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…The superior performance of NNAR models in forecasting various diseases, as compared to SARIMA and HW models, underscores their potential in epidemiological surveillance. This nding aligns with recent advancements in data science and machine learning, which have been increasingly recognized for their ability to enhance disease forecasting and surveillance systems [27][52]- [54].…”

Section: Discussionsupporting

confidence: 70%

Evaluating the Underreporting of Diseases in the Philippines During the COVID-19 Pandemic through Comparative Forecast Analysis

Parreño

2024

Preprint

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Background The COVID-19 pandemic has significantly impacted global health systems, particularly in the Philippines. The redirection of healthcare resources towards COVID-19 has raised concerns about the potential underreporting and surveillance of other communicable diseases. This study explores whether the pandemic has led to the underreporting of these diseases. Methods The study employs Predictive Mean Matching (PMM) for data completeness and uses Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregressive (NNAR), and Holt-Winters (HW) models for disease forecasting. The actual reported cases of diseases for the years 2020 and 2021 are compared with the forecasts to identify discrepancies. Results Significant underreporting was observed for most diseases studied, with notable exceptions such as AFP. NNAR models outperformed SARIMA and HW in forecasting accuracy. Diseases like Measles, Diphtheria, and Rubella showed substantial underreporting, while vector-borne diseases like Dengue and Chikungunya, and waterborne diseases such as Typhoid Fever and Cholera, also indicated underreporting. Conclusions The study reveals significant underreporting of various diseases in the Philippines during the COVID-19 pandemic. The effective use of advanced predictive models underscores the potential of these tools in enhancing disease surveillance and highlights the need for robust health systems capable of sustaining surveillance during crises.

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Section: Discussionsupporting

confidence: 70%

Evaluating the Underreporting of Diseases in the Philippines During the COVID-19 Pandemic through Comparative Forecast Analysis

Parreño

2024

Preprint

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“…(1) Box–Jenkins ARIMA Modelling Approach . The ARIMA ( p , d , q ) [ 35 , 36 ] can be represented by

…”

Section: Methodsmentioning

confidence: 99%

“…NNAR ( p , 0) equals ARIMA ( p , 0,0) without stationarity parameterization. The mathematical expression for NNAR ( p , r ) [ 36 , 44 – 46 ] is of the form

…”

Section: Methodsmentioning

confidence: 99%

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

Tawiah

Daniyal

Qureshi

2023

Journal of Environmental and Public Health

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Pakistan is considered among the top five countries with the highest CO2 emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO2 emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO2 emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistan’s CO2 emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO2 emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO2, initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO2 emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO2 emissions.

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“…Te neural network autoregressive (NNAR) [36][37][38] model is an application of neural networks in supervised classifcation, prediction, and nonlinear time series forecasting. A simple feedforward neural network's design can be characterized as a network of neurons arranged in input, hidden, and output layers in a specifc order [36]. Each layer uses weights that are acquired using a learning method to relay information to the subsequent layer [37].…”

Section: Neural Network Autoregressive (Nnar) Modelmentioning

confidence: 99%

“…, Y K is a subdivision of our data. Te model having the least MSE, RMSE, and MAE is chosen as the preferred model with our data [36][37][38]. R version 4.3.1 was used for all analysis.…”

Section: Multilayer Perceptron (Mlp) Modelmentioning

confidence: 99%

A Comparative Analysis of Traditional SARIMA and Machine Learning Models for CPI Data Modelling in Pakistan

Qureshi,

Khan,

Daniyal

et al. 2023

Applied Computational Intelligence and Soft Computing

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Background. In economic theory, a steady consumer price index (CPI) and its associated low inflation rate (IR) are very much preferred to a volatile one. CPI is considered a major variable in measuring the IR of a country. These indices are those of price changes and have major significance in monetary policy decisions. In this study, different conventional and machine learning methodologies have been applied to model and forecast the CPI of Pakistan. Methods. Pakistan’s yearly CPI data from 1960 to 2021 were modelled using seasonal autoregressive moving average (SARIMA), neural network autoregressive (NNAR), and multilayer perceptron (MLP) models. Several forms of the models were compared by employing the root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) as the key performance indicators (KPIs). Results. The 20-hidden-layered MLP model appeared as the best-performing model for CPI forecasting based on the KPIs. Forecasted values of Pakistan’s CPI from 2022 to 2031 showed an astronomical increase in value which is unpleasant to consumers and economic management. Conclusion. The increasing CPI trend observed if not addressed will trigger a rising purchasing power, thereby causing higher commodity prices. It is recommended that the government put vibrant policies in place to address this alarming situation.

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Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

Cited by 13 publications

References 44 publications

Evaluating the Underreporting of Diseases in the Philippines During the COVID-19 Pandemic through Comparative Forecast Analysis

Evaluating the Underreporting of Diseases in the Philippines During the COVID-19 Pandemic through Comparative Forecast Analysis

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

A Comparative Analysis of Traditional SARIMA and Machine Learning Models for CPI Data Modelling in Pakistan

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