scholarly journals Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler

2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Edre MA ◽  
Muhammad Adil ZA ◽  
Jamalludin AR
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Movement Control ◽  
Time Frame ◽  
Control Measures ◽  
Model Fit ◽  
Quality Of Data ◽  
Auto Regressive ◽  
Control Order ◽  
Time Frames

INTRODUCTION: Coronavirus disease (COVID-19) is a novel pandemic that affects every other country in the world. Various countries have adopted control measures involving restriction of movement. Several studies have used mathematical modelling to predict the dynamic of this pandemic. Forecasting techniques can be used to predict the incidence cases for the short term. The study aims to forecast the COVID-19 incidence using the Auto Regressive Integrated Moving Average (ARIMA) method. MATERIALS AND METHODS: Using publicly available data, we performed a forecast of Malaysia COVID-19 new cases using Expert Modeler Method in SPSS and ARIMA model in R to predict COVID-19 cases in Malaysia. We compare 3 different time frames based on different Movement Control Order (MCO) period. We compare the model fit and prediction across models. RESULTS: All models show static cases for each MCO 7-day prediction. For prediction until 12 May, the third MCO time frame shows the best model fit for both techniques. Both software shows a stationary trend of cases of below 100. CONCLUSION: These MCO models have shown to stabilize the rate of new cases. Further sub analysis and quality of data is needed to improve the accuracy of the model.

scholarly journals Lockdown 2.0 In Malaysia: Evaluating Forecast Performance of Goods Export with Box-Jenkins Methodology and ARIMA Model

2021 ◽  
Vol 3 (2) ◽  
pp. 60-73
Author(s):  
Muhammad Nadzif Ramlan
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Movement Control ◽  
Information Criterion ◽  
Forecast Performance ◽  
Positive Rate ◽  
Moving Average Model ◽  
Control Order ◽  
Performance Rate ◽  
The Empirical Analysis

The purpose of this study is to model the forecast of Malaysia's export of goods using Autoregressive Integrated Moving Average Model (ARIMA) modelling with Box-Jenkins method. The time-series concerned is from the first quarter of 2015 to the first quarter of 2021 based on the Department of Statistics Malaysia (DOSM) data. The empirical analysis focuses on the five criteria for consideration towards the best model: high significant coefficient, high adjusted R-squared value, low sigma squared value, low Akaike Information Criterion (AIC) and low Schwarz Information Criterion (SIC). The study showed that ARIMA (2,1,2) would be the best model to forecast Malaysian export of goods from the second quarter of 2021 to the fourth quarter of 2022. The quarterly forecast opined the performance rate of Malaysian goods export to be at a stable positive rate of 4.9% throughout 2022, indicating the economic recovery progress that Malaysia would acquire from its vaccination programme and Movement Control Order (MCO) done in the previous year. The annual forecast showed a more precise value after comparing the actual and forecast growth value of exports in 2021. This finding is further supported with qualitative analysis about the validity of the forecast values via reports released by sources such as World Bank and Focus Economics.

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.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Monsoon Impact on the Air Quality During SAR-CoV-2 Pandemic Spread in Central Peninsular Malaysia and Sabah: Pre, During and Post Lockdown Scenarios

2021 ◽  
Vol 7 (3) ◽  
pp. 57-68
Author(s):  
M.V. Prasanna ◽  
S. Chidambaram
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Movement Control ◽  
Peninsular Malaysia ◽  
Hysplit Model ◽  
High Precipitation ◽  
Control Order ◽  
Time Frames ◽  
Relationship Of ◽  
The Relationship

Malaysia is considered as one of the countries with the highest novel corona virus (COVID-19) infected cases in Southeast Asia. Recent studies have identified that the air quality of a region also governs the transmission of the virus through pollutants. Hence, a study was conducted to assess the influence of air quality on the COVID-19 pandemic spread in central Peninsular Malaysia and Sabah. An attempt was also made to infer the effect of monsoonal precipitation on air quality. Central Peninsular Malaysia consists of major cities like Kuala Lumpur, Selangor and Putrajaya. These cities are highly populated, with the expansion of industrial activities, rapid urbanisation and greater usage of vehicles has resulted in air quality deterioration. Such conditions have led to related public health issues, compared to Sabah in east Malaysia. In this study, COVID-19 infected cases, air quality index (AQI) and precipitation data were collected from 25 January to August 2020 to infer the relationship of air quality to the pandemic spread before, during and after the implementation of lockdown periods in the country, referred as movement control order (MCO). The lockdown periods fall under various monsoon climate patterns in the country. Interpretation of data reveals that the variation in air quality correlates with the infected cases. Improved air quality was observed during the last phase of MCO with a lesser number of infected cases. The HYSPLIT model was adopted to study the backward air mass trajectories for different time frames to identify the variation in the sources of pollutants reaching the study area. The study determined that the air pollutants have reached the study area from various directions, reflecting a mixed contribution from the ocean and land area. The relationship between high precipitation (during inter-monsoon and SW monsoon) and improved air quality reveals the washout effect of air pollutants. The outcome of this study inferred that the variation of air quality and precipitation rates facilitate the pandemic spread in this region in addition to the other meteorological factors, apart from individual immune capacity and social distancing.

scholarly journals Growth, instability and forecast of marine products export from India

2016 ◽  
Vol 63 (4) ◽  
Author(s):  
Apu Das ◽  
Nalini Ranjan Kumar ◽  
Prathvi Rani
Keyword(s):  
Moving Average ◽  
Export Performance ◽  
Arima Model ◽  
South East Asia ◽  
Arima Models ◽  
Policy Makers ◽  
Increasing Trend ◽  
Marine Products ◽  
Auto Regressive ◽  
Instability Indices

This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India’s marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India’s marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

scholarly journals Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia

2020 ◽  
Vol 17 (15) ◽  
pp. 5509 ◽  
Author(s):  
Balvinder Singh Gill ◽  
Vivek Jason Jayaraj ◽  
Sarbhan Singh ◽  
Sumarni Mohd Ghazali ◽  
Yoon Ling Cheong ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Model Calibration ◽  
Reproduction Number ◽  
Close Contact ◽  
Movement Control ◽  
Control Measures ◽  
Seir Model ◽  
Epidemic Curve ◽  
Reproduction Numbers ◽  
Control Order

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

scholarly journals Analysis and Forecast of Mining Fatalities in Cherat Coal Field, Pakistan

2021 ◽  
Vol 11 (4) ◽  
pp. 22-26
Author(s):  
Sajid Khan ◽  
Kausar Sultan Shah ◽  
Naeem Abbas ◽  
Abdur Rahman ◽  
Naseer Muhammad Khan
Keyword(s):  
Management System ◽  
Safety Management ◽  
Moving Average ◽  
Arima Model ◽  
Coal Field ◽  
Safety Management System ◽  
Term Forecast ◽  
Auto Regressive ◽  
Mineral Production

Mineral exploitation contributes to the economic growth of developing countries. Managing mineral production brought a more disturbing environment linked to workers' causalities due to scarcities in the safety management system. One of the barriers to attaining an adequate safety management system is the unavailability of future information relating to accidents causing fatalities. Policymakers always try to manage the safety system after each accident. Therefore, a precise forecast of the number of workers fatalities can provide significant observation to strengthen the safety management system. This study involves forecasting the number of mining workers fatalities in Cherat coal mines by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Workers' fatalities information was collected over the period of 1994 to 2018 from Mine Workers Federation, Inspectorate of Mines and Minerals and company records to evaluate the long-term forecast. Various diagnostic tests were used to obtain an optimistic model. The results show that ARIMA (0, 1, 2) was the most appropriate model for workers fatalities. Based on this model, casualties from 2019 to 2025 have been forecasted. The results suggest that policymakers should take systematic consideration by evaluating possible risks associated with an increased number of fatalities and develop a safe and effective working platform.

scholarly journals PERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2017 ◽  
Vol 14 (4) ◽  
pp. 524 ◽  
Author(s):  
Djawoto Djawoto
Keyword(s):  
Time Series ◽  
Price Index ◽  
Inflation Rate ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Auto Regressive ◽  
Auto Regression

Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).

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