scholarly journals Prediction of fatal accidents in Indian factories based on ARIMA

2018 ◽  
Vol 18 (18) ◽  
pp. 24-30
Author(s):  
Rajaprasad SVS
Keyword(s):  
Prevention Program ◽  
Moving Average ◽  
Arima Model ◽  
Accident Prevention ◽  
Suitable Model ◽  
Fatal Accidents ◽  
Policy Makers ◽  
Accident Data ◽  
Extent Of Damage ◽  
The Cost

Abstract The inherent benefits of an accident prevention program are generally known only after an accident has occurred. The purpose of implementation of the program is to minimize the number of accidents and cost of damages. Allocation of resources to implement accident prevention program is vital because it is difficult to estimate the extent of damage caused by an accident. Accurate fatal accident predictions can provide a meaningful data that can be used to implement accident prevention program in order to minimize the cost of accidents. This paper forecast the fatal accidents of factories in India by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Accident data for the available period 1980 to 2013 was collected from the Labour bureau, Government of India to analyze the long term forecasts. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (0, 0, 1) is suitable model for prediction of fatal injuries. The number of fatal accidents is forecasted for the period 2014 to 2019. These results suggest that the policy makers and the Indian labour ministry must focus attention toward increasing fatal accidents and try to find out the reasons. It is also an opportunity for the policy makers to develop policies which may help in minimizing the number fatal accidents.

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.

scholarly journals Modelling the Impact of Government Policies on Import on Domestic Price of Indian Gold Using ARIMA Intervention Method

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Jyothi Unnikrishnan ◽  
Kodakanallur Krishnaswamy Suresh
Keyword(s):  
Current Account Deficit ◽  
Moving Average ◽  
Arima Model ◽  
Trade Deficit ◽  
Government Policies ◽  
Suitable Model ◽  
Domestic Price ◽  
Gold Market ◽  
The Government ◽  
The Impact

The study attempts to determine the impact of government policies of import of gold in India on the domestic price of gold during 2013 using Autoregressive Integrated Moving Average (ARIMA) intervention model. 2013 was an amazing year for Indian gold market where the price had reached its zenith. In April 2013, to curb a record trade deficit, India imposed an import duty of 10 percent on gold and tied imports for domestic consumption to exports, creating scarce supply of the yellow metal and boosting premiums to curtail the Current Account Deficit (CAD). The objective of the paper is to model the impact of this intervention by the government on the domestic price of Indian gold. Suitable ARIMA model is fit on the preintervention period and thereafter the effects of the interventions are analysed. The results indicate that ARIMA(1,1,1)is the most suitable model during preintervention period. Intervention analysis reveals that there is significant decrease in domestic price of gold by 56% from 2013. The model may be used by policymakers to analyse the future of gold before framing regulations and policies.

scholarly journals Forecasting Monthly Gold Prices using ARIMA Model: Evidence from Indian Gold Market

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1373-1376
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Gold Price ◽  
Data Series ◽  
Suitable Model ◽  
Natural Logarithm ◽  
Price Series ◽  
Gold Market ◽  
Sample Data ◽  
Augmented Dickey Fuller

In this paper an attempt has been made to give an overview of the Indian gold market so as to develop a model enabling the forecast of gold prices in India. One troy ounce is equal to 31.103 grams. The monthly sample data of gold price (in INR per troy ounce) is taken from December 1997 to December 2017.The entire data has been divided into two segments for estimation and validation sample and to find out the efficiency and accuracy of forecasting models. Since the gold price data series have shown much deviation after March 2006 the first segment of the data is taken from the time period of December 1997 to March 2006 and second segment from April 2006 to December 2017.Due to a larger value and a huge time span of the sample data, the natural logarithm of gold price has been taken to conduct the study and build an effective model to forecast future gold prices. The unit root tests of Augmented Dickey Fuller‖ and Philips Perron have been used to test the gold price series as stationary or non-stationary. It is observed that series are stationary at first difference in both the methods. At first difference the ACFs and PACFs were pattern less and statistically not significant. Box-Jenkins’s Autoregressive Integrated Moving Average of Box-Jenkins methodology has been used for developing a forecasting model of gold price in India. Different models of ARIMA have been used to obtain best suitable model for forecasting using Eviews software 10 for both time periods i.e., December 1997 to March 2006 & April 2006 to December 2017

A Stochastic Model to Analyze and Predict Transmission Dynamics of Tuberculosis in Ede Kingdom of Osun State

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Kehinde Adekunle Bashiru
Keyword(s):  
Process Model ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Probability Generating Function ◽  
Information Criteria ◽  
Data Series ◽  
Suitable Model ◽  
Birth Process ◽  
Autoregressive Integrated Moving Average

In this study the stochastic process model for estimating the incidence of tuberculosis (TB) infection in Ede kingdom (Ede North and Ede South Local Government Areas) of Osun State was carried out. The probability generating function approach was used to solve the associated birth process model to obtain the estimate of TB incidence. Also time series analysis was carried out using JMulti software to predict future incidence rate of the disease in the study area. Based on Autoregressive Integrated Moving Average (ARIMA) model, the autocorrelation and partial autocorrelation methods and a suitable model to forecast TB infection was obtained.  The goodness of fit was measured using the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Having satisfied all the model assumptions ARIMA (0,1,1) model with standard error, 6.37086 was found to be the best model for the forecast. It was observed that the forecasted series were close to the actual data series

scholarly journals Relationship between Gross Domestic Product and Accident in China

2017 ◽  
Vol 3 (1) ◽  
pp. 19 ◽  
Author(s):  
Rujun Wang ◽  
Jinqiu Gong ◽  
Yu Wang ◽  
Haodong Chen ◽  
Sining Chen ◽  
...  
Keyword(s):  
Economic Development ◽  
Gross Domestic Product ◽  
Moving Average ◽  
Arima Model ◽  
Domestic Product ◽  
State Administration ◽  
Chemical Accident ◽  
Accident Data ◽  
Auto Regressive ◽  
The Relationship

The accident and death data from 2002 to 2015 were obtained from State Administration of Work Safety of China to investigate the relationship between gross domestic product (GDP) and accident. The statistical analysis shows that the accident, death and the death rate of per hundred million yuan present an exponential decreasing trend with the increase of national GDP. The chemical accident data in different provinces were further analyzed. It shows that the dangerous chemical accidents primarily distribute in the regions with better economic development, so more safety measures should be taken to prevent the accidents during economic development. In addition, the next three years of accidents were predicted based on auto-regressive integrated moving average (ARIMA) model. The results show that the following two years accidents predicted will be reduced by 4.3% and 6.5% than the last year.

Predicting methane emissions from livestock in Malaysia using the ARIMA model

2014 ◽  
Vol 25 (5) ◽  
pp. 585-599 ◽  
Author(s):  
Rafiu O. Yusuf ◽  
Zainura Zainon Noor ◽  
Ahmad Halilu Abba ◽  
Mohd Ariffin Abu Hassan ◽  
Mohammed Rafee Majid ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Methane Emissions ◽  
Policy Makers ◽  
Content Type ◽  
Autoregressive Integrated Moving Average ◽  
Ipcc Guidelines ◽  
Future Value ◽  
The Moment ◽  
Cover Up

Purpose – The purpose of this paper is to compute the amount of methane generated from the waste of livestock from 1980 to 2008; then use the information in forecasting subsequent methane emissions by the sector in Malaysia from 2009 to 2020. Design/methodology/approach – The research study employed two approaches; computing methane emissions from 1980 to 2008 using the IPCC guidelines, and forecasting methane emissions for the animals from 2009 to 2020 using the autoregressive integrated moving average (ARIMA) model from the predictive analysis software (PASW-SPSS 18.0). Findings – Methane emissions from cattle, buffaloes and pigs accounted for over 95 per cent of total emissions and emissions from cattle are predicted to increase from 67.0 Gg in 2009 to 77.0 Gg by 2020. Emissions from the others will not be appreciable although poultry emissions will rise to 11.0 Gg by 2020. Attempt by the Malaysian Government to increase cattle production is not necessary at the moment as protein requirement has been met. Research limitations/implications – ARIMA model suffers from linear and data limitation: the future value of a variable assumed to be a linear function of several past observations in ARIMA is sometimes unrealistic. Large amounts of historical data are needed in ARIMA models in order to get desired results. The inventory of the animals was taken from 1980 to 2008.This needs to be improved upon by updating it to cover up to 2011 so that the forecast will start from 2012. Practical implications – The chosen ARIMA method has demonstrated its correctness in being adequate as a predicting tool for animal methane emissions. Policy makers can apply it so as to take practical steps to avoid these emissions. Originality/value – This is a novice idea as animal methane emission forecasting tool. This model will be of immense use and help in predicting methane emissions from livestock.

scholarly journals Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model

2021 ◽  
Vol 6 (2) ◽  
pp. 47-56
Author(s):  
Olufunke G. Darley ◽  
Abayomi I. O. Yussuff ◽  
Adetokunbo A. Adenowo
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Test Period ◽  
Suitable Model ◽  
Daily Data ◽  
Moving Average Model ◽  
Time Series Approach ◽  
Short Term Forecasting ◽  
Price Analysis

Abstract This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

Commander's Accident Prevention Program

1989 ◽  
Author(s):  
ARMY SAFETY CENTER FORT RUCKER AL
Keyword(s):  
Prevention Program ◽  
Accident Prevention

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.

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