Modelling and Forecasting Daily Mortality Rate of Covid-19 in Ecowas: Evidence From Arima Model

Nwuju, Kingdom; Better, Ifeoma; Etuk, Ette Harrison

doi:10.46654/ij.24889849.e61214

Cited by 3 publications

(3 citation statements)

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“…Literature [18,19] combined the BP neural network to pre-dict water resources in a certain province, and the results show that this model is suitable for predicting water resources. The BP neural network established in reference [20] has relatively low prediction error for the power plant, and the model is considerable. Literature [21] established the BP neural network fusion prediction model, and literature [22] predicted the demand of the built-up area in a province, and the prediction result was ideal.…”

Section: Related Workmentioning

confidence: 99%

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Hua

2022

Wireless Communications and Mobile Computing

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GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.

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Section: Related Workmentioning

confidence: 99%

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Hua

2022

Wireless Communications and Mobile Computing

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“…The Ecological Footprint addresses this issue by measuring the ecological issues of different crops at variable scales. When measuring the ecological footprint of many countries, the ecological footprint method is adopted, which is an intuitive and effective method [10][11].…”

Section: Ecological Footprintmentioning

confidence: 99%

Water Resources Ecological Footprint Based on ARIMA Model

2022

AJEB

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Water resources are an important part of maintaining the normal operation of ecosystems and the normal development of human society. With the continuous development of social economy, water shortage and water pollution have increasingly attracted the attention of the international community. The purpose of this work is to study the prediction of the ecological footprint of water resources based on the ARIMA model. Based on the ecological footprint theory, a water resources ecological footprint prediction model is established to provide specific reference for the planning goals of sustainable water resource utilization in the future. By studying the time series analysis method, that is, using the eviews software to establish a time series analysis model ARIMA (p, d, q) model, a short-term forecast of M province from 2018 to 2021 is made. According to the time series analysis The prediction results of the model ARIMA (4, 1, 2) show that: from 2018 to 2021, the ecological footprint in M province is on the rise, and the water crisis situation will become increasingly severe. The ARIMA model has a good prediction effect.

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“…The advancement of every economy is dependent on the operations of related macroeconomic variables such as exchange rate, interest rate and inflation which work together to determine economic growth. According to Nwuju, Ifeoma, Lekara-Bayo and Etuk (2019) the exchange rate of a national currency relative to another currency has a direct bearing on the national economy because of bilateral trade relationship between both countries. When the exchange rate of the foreign currency rises with respect to the domestic currency, more number of units of domestic currency are required for one unit of the foreign currency.…”

Section: Introductionmentioning

confidence: 99%

Oil Price Bubbles and Exchange Rate Movement in Nigeria

2022

NIU JSS

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The study examined the impact of oil price bubble on naira exchange rate in Nigeria for the period of 1990-2020. Secondary data was collected from World development indicators and CBN statistical bulletin. Descriptive statistics, unit root test, co-integration test, granger causality test, variance decomposition and vector autoregressive model were conducted. Findings revealed that first and second period lag of oil price bubble have negative effect on exchange rate movement in Nigeria while first period lag was not significant, second period lag was significant at 5% level. The study recommended that there is need for policymakers to be cognizant of oil prices in determining appropriate exchange rate equilibrium. Thus, when oil price changes take place, relevant monetary policy measures should be employed to stabilize the unanticipated impacts on exchange rates that may distort the economy. In addition, diversifying away from oil to other non-oil activities that would generate foreign exchange should be a continuous policy pursuit. Also, the revenue generated from oil should be channelled into creation of necessary and adequate infrastructural facilities in order to encourage small industries in producing locally made goods. By this, the importation of goods will reduce and the trade balance becomes favourable.

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Modelling and Forecasting Daily Mortality Rate of Covid-19 in Ecowas: Evidence From Arima Model

Cited by 3 publications

References 0 publications

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Water Resources Ecological Footprint Based on ARIMA Model

Oil Price Bubbles and Exchange Rate Movement in Nigeria

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