Hull-WEMA: a novel zero-lag approach in the moving average family, with an application to COVID-19

Hansun, Seng; Charles, Vincent; Gherman, Tatiana; Varadarajan, Vijayakumar

doi:10.1504/ijmdm.2022.119582

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…To further determine whether the performance results between each applied deep learning method are significantly different or not, we conducted a paired sample t-test. A Microsoft Excel add-in named 'data analysis toolpak' [37] was used. Using the tool, we compared the MAE, RMSE, and MAPE results between LSTM-Bi-LSTM, LSTM-GRU, and Bi-LSTM-GRU.…”

Section: Discussionmentioning

confidence: 99%

Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches

Hansun

Wicaksana

2022

J Big Data

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As a new type of currency introduced in the new millennium, cryptocurrency has established its ecosystems and attracts many people to use and invest in it. However, cryptocurrencies are highly dynamic and volatile, making it challenging to predict their future values. In this research, we use a multivariate prediction approach and three different recurrent neural networks (RNNs), namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTM), and the gated recurrent unit (GRU). We also propose simple three layers deep networks architecture for the regression task in this study. From the experimental results on five major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Tether (USDT), and Binance Coin (BNB), we find that both Bi-LSTM and GRU have similar performance results in terms of accuracy. However, in terms of the execution time, both LSTM and GRU have similar results, where GRU is slightly better and has lower variation results on average.

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Section: Discussionmentioning

confidence: 99%

Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches

Hansun

Wicaksana

2022

J Big Data

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“…The COVID-19 pandemic continues to attract the attention of researchers, policymakers, private organizations, and individuals as it has significantly impacted the economic, social, and environmental well-being of the world. The coronavirus remains one of the most significant problems facing the world that must be tackled, with much of the current focus dedicated to forecasting the trends and patterns of the COVID-19 pandemic (Hansun et al ., 2021a, b). Among others, the pandemic has posed tremendous challenges to SC disruption, and all nations have been fighting it using different strategies based on their available resources.…”

Section: Conclusion Limitations and Future Scopementioning

confidence: 99%

Navigating COVID-19: unraveling supply chain disruptions through best-worst method and fuzzy TOPSIS

Ali

Charles

Modibbo

et al. 2023

BIJ

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PurposeThe COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).Design/methodology/approachTo determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.FindingsThe study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).Research limitations/implicationsThis study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.Originality/valueThis study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

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“…On the other hand, medical measures are primarily related to the hospitalization of the infected people and treatment thereafter. The future course of Covid-19 is still unknown and in the absence of a universal vaccine, the non-medical measures remain important as a first line of defense against the pandemic (Hansun et al, 2022 ). They relieve the health sector of the imminent pressure by curbing the peak of the outbreak and spreading it over a longer period of time (Fong et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Examining operational efficiency with prudent risks of Covid-19: a contextual DEA analysis with an undesirable intermediate measure

2023

Self Cite

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In the wake of the losses of human lives and disruption to the world economy caused by the spread of the COVID-19 pandemic, it has become imperative to assess the effectiveness of containment strategies adopted by countries. The success of any containment strategy of achieving low mortality and high recovery rate depends on the efficient utilization of available but limited resources, such as number of hospital beds and healthcare workers. While the spreading pattern of the pandemic has been researched heavily, there is limited research that comprehensively focuses on the efficient utilization of available resources to achieve the desired aims of low mortality and high recovery. In order to close this research gap, we employ a two-stage network data envelopment analysis (DEA) to identify the inefficiency in the process and resolve the resource constraints by considering medical and non-medical (administrative) interventions as two serial stages. The number of infected people is treated as the intermediate product, which is an undesirable output of the first stage and subsequently enters the second stage as an input. This network DEA model successfully addresses the conflict between the two stages over the handling of infected people and assesses the vulnerabilities of the countries against the transmission rates of the disease in the respective countries. Thus, the objective of this study is to develop a well-coordinated plan for different government agencies to jointly mitigate the risk under constrained resources. The findings reveal that almost of the Organization for Economic Cooperation and Development (OECD) countries have used their resources suboptimally and are producing, on average, almost half the amount of the maximum possible outputs. As a sizeable amount of inefficiency can be explained by varying economic and demographic factors, such as health expenditure and the proportion of the aged population, the efficiency evaluation has been revisited with adjustments for unfavorable externalities. The analysis and its implications can help policymakers formulate optimal resource plans and identify potential areas for improvement.

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Hull-WEMA: a novel zero-lag approach in the moving average family, with an application to COVID-19

Cited by 4 publications

References 18 publications

Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches

Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches

Navigating COVID-19: unraveling supply chain disruptions through best-worst method and fuzzy TOPSIS

Examining operational efficiency with prudent risks of Covid-19: a contextual DEA analysis with an undesirable intermediate measure

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