Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA

Singh, Pawan Kumar; Chouhan, Anushka; Bhatt, Ramesh Chandra; Kiran, Ravi; Ahmar, Ansari Saleh

doi:10.1007/s11135-021-01207-6

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…SutteARIMA is used in this study based on the ability of this method to predict data in economics, business, and other fields. This can be seen from Ahmar and Boj [7], Ahmar et al [8], Singh et al [9], and Shih et.al. [10] research, which shows that the SutteARIMA method has more accurate prediction results when compared to other methods [7]- [9].…”

Section: Introductionmentioning

confidence: 77%

“…This can be seen from Ahmar and Boj [7], Ahmar et al [8], Singh et al [9], and Shih et.al. [10] research, which shows that the SutteARIMA method has more accurate prediction results when compared to other methods [7]- [9]. Based on this, researchers are interested in comparing the SutteARIMA method with other methods on other data, namely the estimation of welfare cost data.…”

Section: Introductionmentioning

confidence: 77%

“…This method is a combination of the -Sutte Indicator and ARIMA Box-Jenkins methods. SutteARIMA was developed by Ansari Saleh Ahmar and Eva Boj del Val in 2019 [7]- [9]. The following is described by the SutteARIMA method mathematically.…”

Section: -Sutte Indicatormentioning

confidence: 99%

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Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters

Annas

Ahmar

Hidayat

2023

JOIV : Int. J. Inform. Visualization

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Unhealthy and unsafe sanitation will make it easier for various diseases to attack the body. In addition, unsafe sanitation will also affect a country's economy, including declining welfare, tourism losses, and environmental losses due to the loss of productive land. The research aimed to estimate the welfare cost of premature deaths based on unsafe sanitation risks using the SutteARIMA, Neural Network Time Series, and Holt-Winters. The study analyzed estimates and projections of the welfare cost of premature deaths based on the risks of unsafe sanitation of BRICS countries (Brazil, Russia, Indonesia, China, and South Africa). The data in this research used secondary data. Secondary time series data was taken from the Environment Database of the OECD. Stat. (Mortality and welfare cost from exposure to environmental risks). The data on the study was based on variables: welfare cost of premature deaths, % GDP equivalent, risk: unsafe sanitation, age: all, sex: both, unit: percentage, and data from 2005 to 2019. The three forecasting methods (SutteARIMA, Neural Network Time Series, and Holt-Winters) were juxtaposed in fitting data to see the forecasting methods' reliability and accuracy. The accuracy of forecasting results was compared based on MAPE and MSE values. The results of the research showed that the SutteARIMA and NNAR(1,1) methods were best used to predict the welfare cost of premature deaths in view of unsafe sanitation risks for BRICS countries.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 77%

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Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters

Annas

Ahmar

Hidayat

2023

JOIV : Int. J. Inform. Visualization

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“…Furthermore, in the pandemic Covid-19 situation BPNN technique has been used to forecasting of U.S oil markets (Wu et al 2021a, b). Likewise, ARIMA method used to forecast the US stock market progresses (Singh et al 2021), Convolutional neural network method used to forecast crude oil price (Wu et al 2021a). The definition, advantages and disadvantages of the different models can be expressed in Table 4.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Sivakumar¹,

Singh

Arulkirubakaran

et al. 2022

Qual Quant

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The pandemic recession has caused enormous disturbances in many industrialized countries. The massive disruption of the supply chain of production is affecting manufacturing companies operating in and around India. Particularly the medium-sized bus body building works have been reduced, due to its compound anomalies. The integrated view of the production facility priorities is not an easy task. Since it is difficult for available labour to conduct an entire project, the completion of a production process is delayed. But still, the dilemma remains as to how production managers can correctly interpret the priorities of the facility. Indeed, this is a problem missing from the previous study. Fortunately, in the current competitive environment, it is essentially needed. This study has been used Back Propagation Neural Network (BPNN) approach for predicting production facility priorities. The experimental results confirm the suitability of the model for predicting priorities. A real-world problem is taken into account in making use of the model output. In this sense, this total solution facilitates production managers in assessing and enhancing the production facilities. The findings emphasize the priority of "equipment effectiveness, labour scheduling and communication" in order to strengthen the post-pandemic production facility.

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“… 5 An analysis of the links between the evolution of the pandemic and its effect on the Dow Jones Industrial Index, S &P 500 and Nasdaq-100 indices can be seen in Singh et al. ( 2022 ). …”

mentioning

confidence: 99%

The dynamical relation between price changes and trading volume

2023

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This paper introduces a new method to describe and analyse multidimensional time series based on wavelets. The methodology considers the time series as observations of a functional random variable. The paper generalizes previous research on stock market networks by including asset returns and volume trading as the main variables to study the financial market. The methodology is applied to examine the dynamics and structure of the Nasdaq-100 stock market during the pandemic period 2019/12–2021/12 considering both asset returns and volume trading to model the behaviour of different assets that are part of the index, applying an algorithm that offers better performance than others applied in the clustering literature. The study detects four clusters of firms corresponding with companies sharing common economic activities. The structure of the network reveals a nonlinear relationship between the variables, and the study shows that the main macroeconomic events during the period affect each cluster with different intensity. The change in the patterns of returns and risks and the redistribution of wealth in a highly changing environment are emerging phenomena, which must necessarily be carefully analyzed by public policies, in order to avoid the appearance of bubbles and systemic shocks.

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Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA

Cited by 7 publications

References 31 publications

Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters

Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

The dynamical relation between price changes and trading volume

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