scholarly journals Statistical Analysis and Prediction of COVID-19 outbreak in India using Machine Learning

2020 ◽  
pp. 199-207
Author(s):  
Akshar Patel ◽  
Dweepna Garg
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Support Vector ◽  
Health Security ◽  
Autoregressive Integrated Moving Average ◽  
Global Pandemic ◽  
Svm Model ◽  
The Government ◽  
Communication Paths ◽  
Model Support

Coronavirus disease globally known as COVID-19 is triggered by SARS-COV2. It is the predominant cause of an extremely dangerous disease that has bothered global health security. It is proposed that COVID-19 might be zoonotically based on the high number of people exposed in Wuhan City, China, to the wet animal market[1]. COVID-19 is a severe acute respiratory disease, transmitted by respiratory secretions and communication paths, as of WHO reports. The disease is spreading throughout the world at a faster pace. The first instance of COVID-19 was firstly discovered and found in Wuhan, Hubei Province, China in December 2019[1]. This paper analyses the outbreak of this disease until June 22, 2020, for India and other top major affected nations and also predictions were made regarding the number of cases for India over the next 17 days i.e from 23 June 2020 to 9 July 2020. Linear Regression model, Support Vector Machine Regressor (SVM) model, Autoregressive Integrated Moving Average (ARIMA) model and Facebook's Prophet model were used for prediction based on the Kaggle downloaded dataset with data collected from January 22, 2020, to June 22, 2020. By 22 June 2020, the disease has spread across more than 200 countries, reporting 12,322 confirmed cases, 45,26,333 recovered cases and 4,72,171 COVID-19 deaths. Assessment of this epidemic allows the Government to take the appropriate steps to curb the threat of this global pandemic.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 1 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Modelling monthly influenza cases in Malaysia

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254137
Author(s):  
Muhammad Adam Norrulashikin ◽  
Fadhilah Yusof ◽  
Nur Hanani Mohd Hanafiah ◽  
Siti Mariam Norrulashikin
Keyword(s):  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Healthcare Sector ◽  
Support Vector ◽  
Test Dataset ◽  
Autoregressive Integrated Moving Average ◽  
Health Organization ◽  
Nonlinear Components

The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

Gold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

Hybrid Prediction Method of Flight Accident Based on ARIMA and SVM

2013 ◽  
Vol 339 ◽  
pp. 756-761
Author(s):  
Jing Shun Duanmu ◽  
Xu Sheng Gan ◽  
Jian Guo Gao
Keyword(s):  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Prediction Method ◽  
Support Vector ◽  
Good Prediction ◽  
Hybrid Prediction ◽  
Accident Prediction ◽  
Svm Model ◽  
Auto Regressive

The main target of flight accident prediction is to prevent the accident. To improve the pertinence and efficiency, the prediction must be strengthened to raise initiative of accident prevention. A hybrid prediction method, based on Auto-Regressive Integrating Moving Average (ARIMA) and Support Vector Machine (SVM), is proposed. First, ARIMA model is established to describe the linear relation in historical data, and then SVM model is built with regard to the residual error of ARIMA model in order to simulate the nonlinear law in historical data. The sum of prediction value of two models is to final hybrid prediction result. An application example show that the model based on this hybrid method has a good prediction to flight accident, and also is better than ARIMA or SVM model.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 3 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model

2020 ◽  
Author(s):  
Lakshmi Rani Kundu ◽  
Most. Zannatul Ferdous ◽  
Ummay Soumayia Islam ◽  
Marjia Sultana
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Global Public Health ◽  
Prevention Measures ◽  
Expected Number ◽  
Upward Trend ◽  
Autoregressive Integrated Moving Average ◽  
Health Authorities ◽  
The Government ◽  
Daily Total

AbstractBackgroundCOVID-19 is one of the most serious global public health threats creating an alarming situation. Therefore, there is an urgent need for investigating and predicting COVID-19 incidence to control its spread more effectively. This study aim to forecast the expected number of daily total confirmed cases, total confirmed new cases, total deaths and total new deaths of COVID-19 in Bangladesh for next 30 days.MethodsThe number of daily total confirmed cases, total confirmed new cases, total deaths and total new deaths of COVID-19 from 8 March 2020 to 16 October, 2020 was collected to fit an Autoregressive Integrated Moving Average (ARIMA) model to forecast the spread of COVID-19 in Bangladesh from 17th October 2020 to 15th November 2020. All statistical analyses were conducted using R-3.6.3 software with a significant level of p< 0.05.ResultsThe ARIMA (0,2,1) and ARIMA (0,1,1) model was adopted for forecasting the number of daily total confirmed cases, total deaths and total confirmed new cases, new deaths of COVID-19, respectively. The results showed that an upward trend for the total confirmed cases and total deaths, while total confirmed new cases and total new death, will become stable in the next 30 days if prevention measures are strictly followed to limit the spread of COVID-19.ConclusionsThe forecasting results of COVID-19 will not be dreadful for upcoming month in Bangladesh. However, the government and health authorities should take new approaches and keep strong monitoring of the existing strategies to control the further spread of this pandemic.

Dwell Time Prediction of Bus Rapid Transit Using ARIMA-SVM Hybrid Model

2014 ◽  
Vol 587-589 ◽  
pp. 1993-1997 ◽  
Author(s):  
Jian Ding ◽  
Min Yang ◽  
Yi Cao ◽  
Si Li Kong
Keyword(s):  
Dwell Time ◽  
Moving Average ◽  
Linear Part ◽  
Arima Model ◽  
Bus Rapid Transit ◽  
Autoregressive Integrated Moving Average ◽  
Nonlinear Part ◽  
Svm Model ◽  
Moving Average Model ◽  
Short Time

The short-time dwell time of BRT is hard to predict. Considering impacts of complex traffic environment, we can predict the value more effectively by using a new hybrid method, which is mixed with ARIMA (Autoregressive Integrated Moving Average Model), predicting the self-relevant linear part and SVM, predicting residual nonlinear part, than the single ARIMA model and SVM model. The dwell times of BRT line1in Chang Zhou have proved this thesis.

Forecasting the spread and total size of confirmed and discharged cases of COVID-19 in Nigeria using an ARIMA model

2021 ◽  
pp. 1-6
Author(s):  
S. Agboola ◽  
P. Niyang ◽  
O. Olawepo ◽  
W. Ukponu ◽  
S. Niyang ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Total Size ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Partial Autocorrelation Function ◽  
The Government ◽  
Global Threat ◽  
Mandatory Use

Coronavirus disease 2019 (COVID-19) has been considered a global threat spreading to Nigeria and posing major public health threats and concerns. This led to the introduction of internationally acceptable non-pharmaceutical interventions (NPI) such as lockdowns, social distancing, and mandatory use of face masks by the Nigerian government to curtail the disease. This study aims to develop an Autoregressive Integrated Moving Average (ARIMA) model to predict COVID-19 cases vis Total Confirmed Cases (TCC) and Total Discharged Cases (TDC) in Nigeria based on the daily data obtained from the Nigeria Centre for Diseases Control (NCDC) from 27th February 2020 to 6th June 2020. The autocorrelation function (ACF), and partial autocorrelation function (PACF) were used to determine the constructed model. An ARIMA model was developed to predict the trend of TCC and TDC for the next 200 days. Forecasting was done using the constructed models. The finding shown that TCC increased to 50,225 with a CI between 29,425 to 100,450 and TDC to 20,186 with CI between 11,106 to 40,366 approximately. The result shows a significant increase in both TCC and TDC from COVID-19 which should guide the government roll out and management of the different NPI and policies to contain the virus.

Modeling River Stream Flow Using Support Vector Machine

2013 ◽  
Vol 315 ◽  
pp. 602-605 ◽  
Author(s):  
Ali Rafidah ◽  
Yacob Suhaila
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Stream Flow ◽  
Moving Average ◽  
Support Vector ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Svm Model ◽  
Record Data ◽  
Better Than

Support Vector Machine (SVM) is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in river stream flow forecasting. In this paper, SVM is proposed for river stream flow forecasting. To assess the effectiveness SVM, we used monthly mean river stream flow record data from Pahang River at Lubok Paku, Pahang. The performance of the SVM model is compared with the statistical Autoregressive Integrated Moving Average (ARIMA) and the result showed that the SVM model performs better than the ARIMA models to forecast river stream flow Pahang River.

scholarly journals Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shiwei Su
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Linear And Nonlinear ◽  
True Values

In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.

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