Pub Date : 2022-12-31DOI: 10.54560/jracr.v12i4.342
Shu-xian Wang, T. Zhang
In the context of a unified large market, the development of digital economy has become the focus. Based on the panel data of 31 provinces in China from 2011 to 2020, this paper uses the spatial Durbin model to examine the impact of government spending on digital economy through different spatial weight matrices. The research results show that: 1) The government science and technology expenditure in all provinces in China has a significant positive spatial correlation with the level of digital economy development; 2) The comparative analysis of different weight matrices show that the impact of local government science and technology expenditure on the level of digital economy development is significant and positive, and there is a certain spatial spillover effect; 3) Through the heterogeneity test, the spillover effect of the central and western regions of China is more obvious under the setting of the geographical weight matrix, and the spillover effect of the east is more obvious under the setting of the economic matrix weight. At the same time, this study expands the research on the influencing factors of the development level of the digital economy, and puts forward policy recommendations corresponding to government science and technology expenditure and the development of the digital economy based on the empirical results.
{"title":"Research on the Spatial Effect of Government Science and Technology Expenditure on the Development of Digital Economy","authors":"Shu-xian Wang, T. Zhang","doi":"10.54560/jracr.v12i4.342","DOIUrl":"https://doi.org/10.54560/jracr.v12i4.342","url":null,"abstract":"In the context of a unified large market, the development of digital economy has become the focus. Based on the panel data of 31 provinces in China from 2011 to 2020, this paper uses the spatial Durbin model to examine the impact of government spending on digital economy through different spatial weight matrices. The research results show that: 1) The government science and technology expenditure in all provinces in China has a significant positive spatial correlation with the level of digital economy development; 2) The comparative analysis of different weight matrices show that the impact of local government science and technology expenditure on the level of digital economy development is significant and positive, and there is a certain spatial spillover effect; 3) Through the heterogeneity test, the spillover effect of the central and western regions of China is more obvious under the setting of the geographical weight matrix, and the spillover effect of the east is more obvious under the setting of the economic matrix weight. At the same time, this study expands the research on the influencing factors of the development level of the digital economy, and puts forward policy recommendations corresponding to government science and technology expenditure and the development of the digital economy based on the empirical results.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88461236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-08DOI: 10.54560/jracr.v12i3.336
Tie Li, Wang Xu
1 Institute of Geophysics, China Earthquake Administration, Beijing (100081), China 2 School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China 3 Guizhou Institution for Technology Innovation & Entrepreneurship Investment, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China * Correspondence: 2fen222@163.com
{"title":"The 10th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention Was Successfully Held in Beijing","authors":"Tie Li, Wang Xu","doi":"10.54560/jracr.v12i3.336","DOIUrl":"https://doi.org/10.54560/jracr.v12i3.336","url":null,"abstract":"1 Institute of Geophysics, China Earthquake Administration, Beijing (100081), China 2 School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China 3 Guizhou Institution for Technology Innovation & Entrepreneurship Investment, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China * Correspondence: 2fen222@163.com","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89326300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-30DOI: 10.54560/jracr.v12i3.332
Wei Li, Cheng-shu Wu, Sumei Ruan
With the continuous expansion of the banks' credit card businesses, credit card fraud has become a serious threat to banking financial institutions. So, the automatic and real-time credit card fraud detection is the meaningful research work. Because machine learning has the characteristics of non-linearity, automation, and intelligence, so that credit card fraud detection can improve the detection efficiency and accuracy. In view of this, this paper proposes a credit card fraud detection model based on heterogeneous ensemble, namely CUS-RF (cluster-based under-sampling boosting and random forest), based on clustering under-sampling and random forest algorithm. CUS-RF-based credit card fraud detection model has the following advantages. Firstly, the CUS-RF model can better overcome the issue of data imbalance. Secondly, based on the idea of heterogeneous ensemble learning, the clustering under-sampling method and random forest model are fused to achieve a better performance for credit card fraud detection. Finally, through the verification of real credit card fraud dataset, the CUS-RF model proposed in this paper has achieved better performance in credit card fraud detection compared with the benchmark model.
随着银行信用卡业务的不断扩大，信用卡诈骗已成为银行业金融机构面临的严重威胁。因此，信用卡欺诈的自动实时检测是一项有意义的研究工作。由于机器学习具有非线性、自动化、智能化的特点，使得信用卡欺诈检测可以提高检测效率和准确性。鉴于此，本文提出了一种基于异构集成的信用卡欺诈检测模型，即基于聚类欠采样和随机森林算法的us - rf (cluster-based undersampling boosting and random forest)。基于cu - rf的信用卡欺诈检测模型具有以下优点:首先，CUS-RF模型可以较好地克服数据不平衡的问题。其次，基于异构集成学习的思想，将聚类欠采样方法与随机森林模型相融合，提高信用卡欺诈检测的性能;最后，通过对真实信用卡欺诈数据集的验证，与基准模型相比，本文提出的cu - rf模型在信用卡欺诈检测方面取得了更好的性能。
{"title":"CUS-RF-Based Credit Card Fraud Detection with Imbalanced Data","authors":"Wei Li, Cheng-shu Wu, Sumei Ruan","doi":"10.54560/jracr.v12i3.332","DOIUrl":"https://doi.org/10.54560/jracr.v12i3.332","url":null,"abstract":"With the continuous expansion of the banks' credit card businesses, credit card fraud has become a serious threat to banking financial institutions. So, the automatic and real-time credit card fraud detection is the meaningful research work. Because machine learning has the characteristics of non-linearity, automation, and intelligence, so that credit card fraud detection can improve the detection efficiency and accuracy. In view of this, this paper proposes a credit card fraud detection model based on heterogeneous ensemble, namely CUS-RF (cluster-based under-sampling boosting and random forest), based on clustering under-sampling and random forest algorithm. CUS-RF-based credit card fraud detection model has the following advantages. Firstly, the CUS-RF model can better overcome the issue of data imbalance. Secondly, based on the idea of heterogeneous ensemble learning, the clustering under-sampling method and random forest model are fused to achieve a better performance for credit card fraud detection. Finally, through the verification of real credit card fraud dataset, the CUS-RF model proposed in this paper has achieved better performance in credit card fraud detection compared with the benchmark model.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75080685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-30DOI: 10.54560/jracr.v12i3.334
E. Anyika
COVID-19 is still being experienced worldwide, with other variants also cropping up, including Delta and Omicron, etc. Many countries are learning to cope with the mental, physical, and economic impacts of this pandemic by applying recommended health or otherwise protocols in their daily undertakings. The research critically surveyed COVID-19 mental health risks in Africa. Its objectives were to determine the extent to which COVID-19, directly and indirectly, affected the mental health of citizens and to estimate the Mental Health risk levels due to the COVID-19 Pandemic in Africa. In doing so, participants in Africa were sent a Google survey form by WhatsApp. and Seventy-two responses were received. The Depression, Anxiety and Stress Scale-21 (DASS-21) was used to measure participants' mental health risk levels of depression, anxiety, and stress during the COVID-19 pandemic periods up to November 7, 2021. The study discovered that over 90 % of individuals had one form of mental health disorder during the pandemic. In addition, many participants experienced severe depression and anxiety resulting in mental health issues such as dysphoria, anhedonia, and inertia that assesses autonomic arousal, skeletal muscle effects, situational anxiety, and subjective experience of anxious affect.
{"title":"COVID-19 Mental Health Risks - A Critical Survey of Africa","authors":"E. Anyika","doi":"10.54560/jracr.v12i3.334","DOIUrl":"https://doi.org/10.54560/jracr.v12i3.334","url":null,"abstract":"COVID-19 is still being experienced worldwide, with other variants also cropping up, including Delta and Omicron, etc. Many countries are learning to cope with the mental, physical, and economic impacts of this pandemic by applying recommended health or otherwise protocols in their daily undertakings. The research critically surveyed COVID-19 mental health risks in Africa. Its objectives were to determine the extent to which COVID-19, directly and indirectly, affected the mental health of citizens and to estimate the Mental Health risk levels due to the COVID-19 Pandemic in Africa. In doing so, participants in Africa were sent a Google survey form by WhatsApp. and Seventy-two responses were received. The Depression, Anxiety and Stress Scale-21 (DASS-21) was used to measure participants' mental health risk levels of depression, anxiety, and stress during the COVID-19 pandemic periods up to November 7, 2021. The study discovered that over 90 % of individuals had one form of mental health disorder during the pandemic. In addition, many participants experienced severe depression and anxiety resulting in mental health issues such as dysphoria, anhedonia, and inertia that assesses autonomic arousal, skeletal muscle effects, situational anxiety, and subjective experience of anxious affect.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"29 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72607104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-30DOI: 10.54560/jracr.v12i3.333
Zhongfei Zhou, Jiajia Wu, Sheng Yuan
The paper develops a two-type behavioral heterogeneous agent model including fundamentalists and chartists. It examines whether investors’ behavioral heterogeneity is related to the excessive volatility of RMB exchange rate. We use the deviation of the real exchange rate from the fundamental exchange rate as a measure of excessive exchange rate volatility. The fundamental value is calculated by the revised RMB fundamental exchange rate model with cointegration technology. After estimating the behavioral heterogeneous agent model using the monthly RMB exchange rate data from October 2006 to November 2020, we find that the heterogeneity of traders in price and trading strategies can significantly explain excess volatility of the RMB exchange rate. Our analysis of two significant fluctuations in 2015-2016 and 2018-2019 further corroborates our key finding that investors’ behavioral heterogeneity plays an important role in explaining excess volatility of RMB exchange rate.
{"title":"Behavioral Heterogeneity and Excessive Volatility of RMB Exchange Rate","authors":"Zhongfei Zhou, Jiajia Wu, Sheng Yuan","doi":"10.54560/jracr.v12i3.333","DOIUrl":"https://doi.org/10.54560/jracr.v12i3.333","url":null,"abstract":"The paper develops a two-type behavioral heterogeneous agent model including fundamentalists and chartists. It examines whether investors’ behavioral heterogeneity is related to the excessive volatility of RMB exchange rate. We use the deviation of the real exchange rate from the fundamental exchange rate as a measure of excessive exchange rate volatility. The fundamental value is calculated by the revised RMB fundamental exchange rate model with cointegration technology. After estimating the behavioral heterogeneous agent model using the monthly RMB exchange rate data from October 2006 to November 2020, we find that the heterogeneity of traders in price and trading strategies can significantly explain excess volatility of the RMB exchange rate. Our analysis of two significant fluctuations in 2015-2016 and 2018-2019 further corroborates our key finding that investors’ behavioral heterogeneity plays an important role in explaining excess volatility of RMB exchange rate.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75379434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-30DOI: 10.54560/jracr.v12i3.335
Abdallah S. Elgharbawy
A Pure polyvinyl chloride (PVC) is a white, brittle material and it is the third-largest polymers produced after polyethylene and polypropylene as 40 million tons of PVC are produced yearly. The basic structure of PVC is (C2H3Cl)n and it is produced by polymerization of the vinyl chloride monomer (VCM) with a polymerization degree ranges from 300 to 1500. The chlorine content in PVC is about 57% by weight, which makes it less dependent on hydrocarbon content. In this paper, we are going to reveal the PVC additives and applications.
{"title":"Poly Vinyl Chloride Additives and Applications-A Review","authors":"Abdallah S. Elgharbawy","doi":"10.54560/jracr.v12i3.335","DOIUrl":"https://doi.org/10.54560/jracr.v12i3.335","url":null,"abstract":"A Pure polyvinyl chloride (PVC) is a white, brittle material and it is the third-largest polymers produced after polyethylene and polypropylene as 40 million tons of PVC are produced yearly. The basic structure of PVC is (C2H3Cl)n and it is produced by polymerization of the vinyl chloride monomer (VCM) with a polymerization degree ranges from 300 to 1500. The chlorine content in PVC is about 57% by weight, which makes it less dependent on hydrocarbon content. In this paper, we are going to reveal the PVC additives and applications.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73747307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-10DOI: 10.54560/jracr.v12i2.327
Yong Huang, Hongmei Zhang
This paper first theoretically analyzes the influence of the development of Fintech on the operating benefit of banks from the perspective of competition and diminishing marginal benefit, then the annual data of 38 banks listed in China’s A-share market from 2015 to 2020 were selected for empirical and robustness tests by system GMM. The results show that there is a significant "inverted U-shaped" relationship between the development of Fintech and the operating benefit of banks, which is robust and dependable. Finally, relevant feasible suggestions for the development of Fintech in banks are provided, in order to help banks' digital transformation.
{"title":"Research on the Influence of Fintech Development on the Operating Benefit of Banks","authors":"Yong Huang, Hongmei Zhang","doi":"10.54560/jracr.v12i2.327","DOIUrl":"https://doi.org/10.54560/jracr.v12i2.327","url":null,"abstract":"This paper first theoretically analyzes the influence of the development of Fintech on the operating benefit of banks from the perspective of competition and diminishing marginal benefit, then the annual data of 38 banks listed in China’s A-share market from 2015 to 2020 were selected for empirical and robustness tests by system GMM. The results show that there is a significant \"inverted U-shaped\" relationship between the development of Fintech and the operating benefit of banks, which is robust and dependable. Finally, relevant feasible suggestions for the development of Fintech in banks are provided, in order to help banks' digital transformation.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79082648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-10DOI: 10.54560/jracr.v12i2.329
Chengyi Pu, Zihan Ma, Qijuan Luo
1 School of Insurance, Central University of Finance and Economics, Beijing (100081), China 2 College of Finance and Economics, Tibet University, Lhasa (850014), Tibet, China 3 Sichuan Society for Risk Science and Emergency Management, Chengdu (610041), Sichuan, China 4 School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China 5 Guizhou Institution for Technology Innovation & Entrepreneurship Investment, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China * Correspondence: pucy2011@126.com
{"title":"2022 Greater China Region Forum on Corporate Social Responsibility and Social Business Day & Academic Annual Meeting of Sichuan Society for Risk Science and Emergency Management Was Held Online","authors":"Chengyi Pu, Zihan Ma, Qijuan Luo","doi":"10.54560/jracr.v12i2.329","DOIUrl":"https://doi.org/10.54560/jracr.v12i2.329","url":null,"abstract":"1 School of Insurance, Central University of Finance and Economics, Beijing (100081), China 2 College of Finance and Economics, Tibet University, Lhasa (850014), Tibet, China 3 Sichuan Society for Risk Science and Emergency Management, Chengdu (610041), Sichuan, China 4 School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China 5 Guizhou Institution for Technology Innovation & Entrepreneurship Investment, Guizhou University of Finance and Economics, Guiyang (550025), Guizhou, China * Correspondence: pucy2011@126.com","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74469806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-10DOI: 10.54560/jracr.v12i2.325
Akın Menekşe, Hatice Camgöz Akdağ
Hospitals are vital infrastructures, but they can be particularly susceptible if located in an earthquake-prone area. Estimating the performance and projected damage of these structures in the event of future earthquakes is critical to minimize physical damage and disruption of surgery, rehabilitation, laboratory services, and other medical care operations. A quick and low-cost pre-assessment of the sensitivity of these structures will help hospital managements prepare for suitable retrofitting and reconstruction. On the other hand, a seismic risk assessment equires constructing a model that can offer expert opinions in a quantitative, methodical, and quantifiable way while still reflecting the uncertain and imprecise character of the process. This paper proposes a new decision support model by extending Additive Ratio Assessment (ARAS) with interval-valued spherical fuzzy sets. The study includes sensitivity and comparison analyses, practical implications, limitations, and future research avenues. The applicability of our methodology demonstrated a numerical example for assessing the seismic risk levels of hospital buildings.
{"title":"Seismic Risk Analysis of Hospital Buildings: A Novel Interval-Valued Spherical Fuzzy ARAS","authors":"Akın Menekşe, Hatice Camgöz Akdağ","doi":"10.54560/jracr.v12i2.325","DOIUrl":"https://doi.org/10.54560/jracr.v12i2.325","url":null,"abstract":"Hospitals are vital infrastructures, but they can be particularly susceptible if located in an earthquake-prone area. Estimating the performance and projected damage of these structures in the event of future earthquakes is critical to minimize physical damage and disruption of surgery, rehabilitation, laboratory services, and other medical care operations. A quick and low-cost pre-assessment of the sensitivity of these structures will help hospital managements prepare for suitable retrofitting and reconstruction. On the other hand, a seismic risk assessment equires constructing a model that can offer expert opinions in a quantitative, methodical, and quantifiable way while still reflecting the uncertain and imprecise character of the process. This paper proposes a new decision support model by extending Additive Ratio Assessment (ARAS) with interval-valued spherical fuzzy sets. The study includes sensitivity and comparison analyses, practical implications, limitations, and future research avenues. The applicability of our methodology demonstrated a numerical example for assessing the seismic risk levels of hospital buildings.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87118916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-10DOI: 10.54560/jracr.v12i2.326
Funda Ahmetoğlu Taşdemir
The Covid-19 pandemic has brought a lot of concerns about the operational and financial situation of businesses. Forecasting is crucial as it guides businesses through these critical points. Forecasting has become even more critical in the pandemic environment and therefore the necessity of using an accurate forecasting method has increased. Taking this into consideration, in this study, intelligent machine learning methods, namely; Grey Model (GM), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to make a short-term prediction of a food supplement, a product whose demand increased with the pandemic situation. Eighty-five percent of the historical data is used for training purposes and fifteen percent of the data is used for measuring accuracy. The accuracy of the models employed is improved with parameter optimization The accuracy performance indicator Mean Absolute Percentage Error (MAPE) showed that all methods give superior results when the historical data has an increasing sales trend. This study presents an important consideration for businesses and has a potential to be generalized for a business whose sales have an increasing trend not only because of the pandemic but also for any reason.
{"title":"Machine Learning Sales Forecasting for Food Supplements in Pandemic Era","authors":"Funda Ahmetoğlu Taşdemir","doi":"10.54560/jracr.v12i2.326","DOIUrl":"https://doi.org/10.54560/jracr.v12i2.326","url":null,"abstract":"The Covid-19 pandemic has brought a lot of concerns about the operational and financial situation of businesses. Forecasting is crucial as it guides businesses through these critical points. Forecasting has become even more critical in the pandemic environment and therefore the necessity of using an accurate forecasting method has increased. Taking this into consideration, in this study, intelligent machine learning methods, namely; Grey Model (GM), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to make a short-term prediction of a food supplement, a product whose demand increased with the pandemic situation. Eighty-five percent of the historical data is used for training purposes and fifteen percent of the data is used for measuring accuracy. The accuracy of the models employed is improved with parameter optimization The accuracy performance indicator Mean Absolute Percentage Error (MAPE) showed that all methods give superior results when the historical data has an increasing sales trend. This study presents an important consideration for businesses and has a potential to be generalized for a business whose sales have an increasing trend not only because of the pandemic but also for any reason.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85603761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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