Short term Forecast of the COVID-19 Epidemic in top 15 Affected Countries in the World using ARIMA Model with Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Pavan Kumar ◽  
Ranjit Sah ◽  
Alfonso J. Rodriguez-Morales ◽  
Himangshu Kalita Jr ◽  
Akshaya Srikanth Bhagavathula ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Learning Approach ◽  
Average Model ◽  
Machine Learning Approach ◽  
The World ◽  
Moving Average Model ◽  
Auto Regressive

BACKGROUND The COVID-19 pendemic reached more than 200 countries, which was recognized during December-19 from CHINA and affected more than 28 lakh people on date April 26, 2020 (data source:Johns Hopkins Corona Virus Resource Center). OBJECTIVE We here predicted some trajectories of COVID-19 in the coming days (until July 2, 2020) using the most advanced Auto-Regressive Integrated Moving Average Model (ARIMA). METHODS Here we have used the Auto-Regressive Integrated Moving Average Model (ARIMA). Mathematical approaches are widely used to infer critical epidemiological transitions and parameters of COVID-19. Methods such as epidemic curve fitting, surveillance data during the early transmission R0, and other epidemic models are frequently applied to generate forecasts of COVID-19 pandemic across the world. RESULTS Our analysis predicted very frightening outcomes, which defines to worsen the conditions in Iran, entire Europe, especially Italy, Spain, and France. While South Korea, after the initial blast, has come to stability, the same goes for the COVID-19 origin country China with more positive recovery cases and confirm to remain stable. The United States of America (USA) is come as a surprise and going to become the epicenter for new cases during the mid-April 2020. CONCLUSIONS Based on our predictions, public health officials should tailor aggressive interventions to grasp the power exponential growth, and rapid infection control measures at hospital levels are urgently needed to curtail the COVID-19 pandemic. This study analyzed at global level and extracted data upon Machine Learning approach using Artificial intelligence techniques for top 10% or 20 countries.

scholarly journals Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach

2020 ◽  
Author(s):  
Pavan Kumar ◽  
Himangshu Kalita ◽  
Shashikanta Patairiya ◽  
Yagya Datt Sharma ◽  
Chintan Nanda ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Control Measures ◽  
Infection Control Measures ◽  
Machine Learning Approach ◽  
Moving Average Model ◽  
Public Health Officials ◽  
Auto Regressive ◽  
Aggressive Interventions

AbstractWe here predicted some trajectories of COVID-19 in the coming days (until April 30, 2020) using the most advanced Auto-Regressive Integrated Moving Average Model (ARIMA). Our analysis predicted very frightening outcomes, which defines to worsen the conditions in Iran, entire Europe, especially Italy, Spain, and France. While South Korea, after the initial blast, has come to stability, the same goes for the COVID-19 origin country China with more positive recovery cases and confirm to remain stable. The United States of America (USA) will come as a surprise and going to become the epicenter for new cases during the mid-April 2020. Based on our predictions, public health officials should tailor aggressive interventions to grasp the power exponential growth, and rapid infection control measures at hospital levels are urgently needed to curtail the COVID-19 pandemic.

scholarly journals Forecasting Area, Production and Yield of Onion in Bangladesh by Using ARIMA Model

2019 ◽  
pp. 1-12
Author(s):  
Farhana Arefeen Mila ◽  
Mst. Tania Parvin
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Upward Trend ◽  
Average Model ◽  
Domestic Markets ◽  
Moving Average Model ◽  
Auto Regressive ◽  
The Government ◽  
Day By Day

In Bangladesh, onion is the widely used spices both for preparing food and curing diseases as it has medicinal values. As the demand for onion is increasing day by day, it is necessary to make actual projections of onion for undertaking some policies based on it. Therefore, the study investigates the future changes in the area, yield and production of onion in Bangladesh by using the most popular Box-Jenkins methodology. The auto regressive integrated moving average model has been used to understand the pattern of change over a period of 57 years (1961 to 2017) as well as to forecast the changes in the upcoming years. Some information criteria (such as AIC, AICc and BIC) was considered for selecting the best-fitted models of each variable. The forecasted results showed an upward trend for all the variables considered in this study. It implies that the area of onion will increase from 193932.6 hectares in 2018 to 265770.9 hectare in 2027. Again, the amount of onion production will increase from 2073.61 M tons to 3574.06 M tons and for onion yield, it will rise from 10343.17 Kg/ha to 12988.02 kg/ha from 2018 to 2027. These predictions may help the government balancing the demand with the supply and also regulating the price of onion in the domestic markets of Bangladesh.

scholarly journals An An Approach to Measure the Death Impact of Covid-19 in Jakarta using Autoregressive Integrated Moving Average (ARIMA)

2020 ◽  
Vol 9 (2) ◽  
pp. 108-116
Author(s):  
Ferdian Fadly ◽  
Erika Sari
Keyword(s):  
Forecast Error ◽  
Moving Average ◽  
Arima Model ◽  
Average Model ◽  
Real Situation ◽  
Autoregressive Integrated Moving Average ◽  
The Real ◽  
The People ◽  
The World ◽  
Moving Average Model

Coronavirus disease 2019 (COVID-19) is a pandemic in more than 200 countries around the world. As the fourth most populous nation in the world, Indonesia is predicted to face a big threat to this pandemic particularly Jakarta as the epicenter of the virus in Indonesia. However, the nature of COVID-19 that can easily spread and also many undetected cases that do not present symptoms make it more difficult to determine the real mortality effects of COVID-19.The deaths in Jakarta from the new coronavirus may be higher than officially reported. To overcome this issue, this paper will provide an approach to measure the death impact of COVID-19 using the Autoregressive Integrated Moving Average model (ARIMA). The model will predict the ‘what if’ normal condition of the number of funerals in Jakarta compared to the real situation in March 2020 as an approach of the actual effect of COVID-19 in Jakarta. This research revealed a discrepancy of 450-1070 funerals in March 2020 that could not be predicted by the ARIMA model. This funeral gap, a forecast error, could be an approach to the potential number of possible death impacts of COVID-19 in Jakarta that should be significantly higher than the report. The people should be more conscious and alert of COVID-19 situation.

scholarly journals Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3278 ◽  
Author(s):  
Xinyu Han ◽  
Rongrong Li
Keyword(s):  
Energy Consumption ◽  
East Africa ◽  
Moving Average ◽  
Arima Model ◽  
Grey Model ◽  
Systematic Research ◽  
Average Model ◽  
Non Linear ◽  
Moving Average Model ◽  
Auto Regressive

Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

scholarly journals Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2574 ◽  
Author(s):  
Yeqi An ◽  
Yulin Zhou ◽  
Rongrong Li
Keyword(s):  
Power Generation ◽  
Electricity Generation ◽  
Moving Average ◽  
Probabilistic Methods ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Average Model ◽  
Non Linear ◽  
Moving Average Model ◽  
Auto Regressive

With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

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