Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based

Yasin, Hasbi; Warsito, Budi; Santoso, Rukun; Suparti,

doi:10.1051/e3sconf/20187313008

Cited by 6 publications

(4 citation statements)

References 7 publications

(6 reference statements)

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“…An artificial neural network is a network consisting of a collection of processing units called "nodes" that are arranged in certain layers. In a neural network model, the predictor variable or the input is at the bottom layer, while the response variable or the output is at the top layer [32][33][34][35][36][37]. A hidden layer that also contains hidden nodes can be added between the input layer and the output layer as an intermediary for processing the nodes to produce better results [38][39][40].…”

Section: Causal Impact Analysis and Neural Networkmentioning

confidence: 99%

National Vaccination and Local Intervention Impacts on COVID-19 Cases

et al. 2021

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COVID-19, as a global pandemic, has spread across Indonesia. Jakarta, as the capital of Indonesia, is the province with the most positive cases. The government has issued various guidelines, both at the central and regional levels. Since it began in 2021, the planned new measures, called ‘Pemberlakuan Pembatasan Kegiatan Masyarakat Darurat’, or PPKM emergency public activity restrictions, began with the possibility that the number of active cases might decrease. Accordingly, global vaccinations were also carried out, as they were in Indonesia. However, the first phase prioritized frontline health workers and high-risk elderly people. This study conducted a causal impact analysis to determine the effectiveness of PPKM in Jakarta and its vaccination program against the increase in daily new cases. Based on this test, PPKM showed a significant effect on the addition of daily new cases and recovered cases. Conversely, the vaccination program only had a significant impact on recovered cases. A forecast of the COVID-19 cases was conducted and indicated that the daily new cases showed a negative trend, although it fluctuated for the next 7 days, while death and recovered cases continued to increase. Hence, it can be said that the vaccination program has still not shown its effectiveness in decreasing the number of daily new cases while PPKM is quite effective in suppressing new cases.

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Section: Causal Impact Analysis and Neural Networkmentioning

confidence: 99%

National Vaccination and Local Intervention Impacts on COVID-19 Cases

et al. 2021

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“…In this regard, it is proposed for future research that the linear predictive approach using VAR modeling is done on the transformed time series data (we can use, e.g., logarithmic or power transformations), since the transformations may inhibit greater fluctuations and improve linearity in non-linear time series (Shumway & Stoffer, 2017). Future research could furthermore use some other approaches, which allow analyzing non-linear multiple time series, e.g., non-linear VAR models (as described by Kilian & Lütkepohl, 2017) or even VAR neural network models (as described by Yasin et al, 2018), which allow the analysis of non-linear time series without their prior transformation.…”

Section: Turing?mentioning

confidence: 99%

Impacts of the Transformation to Industry 4.0 in the Manufacturing Sector: The Case of the U.S.

Rojko¹,

Erman²,

Jelovac³

2020

Organizacija

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Background and purpose: The transformation to Industry 4.0 increases the number of robots installed within industries, which brings great shifts in industrial ecosystems. For this reason, our research goal was to analyze the key performance indicators to investigate the economic and social sustainability of the changes in production.Methodology: The combination of official (World Bank, U.S. Bureau of Labor Statistics) and publicly available (Federal Reserve Economic Data, Industrial Federation of Robotics) data was used for statistical data processing, including comparison, correlation, cross-correlation and vector autoregression analysis, to present the past developments and also to predict future trends within the U.S. manufacturing sector.Results: In contrast to robust industry robotization observed in the 2008–2018 period, the share of manufacturing output and employment declined. Nonetheless, the vector autoregression model forecast shows, that the U.S. manufacturing sector has arrived at a turning point, after which robotization can increase employment and labor productivity of workers, while also stimulating further growth of their education levels.Conclusion: The transition to Industry 4.0 has a major impact on increasing demands for new knowledge and skills for increased productivity. Accordingly, forecasted growths of analyzed manufacturing indicators suggest that negative impacts of robotization in the recent past were only temporary, due to the entrance to the Industry 4.0 era. Nonetheless, additional policies to support sustainable industry development are required.

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“…Vector autoregressive (VAR) has several endogenous variables simultaneously [37], but each endogenous variable is explained by the lag of its value and other endogenous variables in the model. The VAR model is built to overcome the relationship between variables so that they can still be estimated without the need to emphasize exogenous issues [38]. In this approach, all variables are considered endogenous, and estimates can be carried out simultaneously or sequentially [39].…”

Section: Vector Autoregressivementioning

confidence: 99%

“…IV. ANALYSIS A. PROBABILITY TRANSACTION MARKOV CHAINThe first step is to calculate the probability transition PM 2.5 data, which is classified by No Risk, Medium Risk(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48), and Moderate (> 49). In this paper, we are using monthly data from January 2014 to May 2019 in 2 Taiwanese locations, Pingtung and Chaozhou.…”

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confidence: 99%

Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO

et al. 2019

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Air pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properties and time intervals of the contaminants in the air resulting in disturbances to the lives of humans, plants or animals. One of the parameters measured in determining air quality is PM 2.5. However, PM 2.5 has a higher probability of being able to enter the lower respiratory tract because small particle diameters can potentially pass through the lower respiratory tract. In this paper, we will get two different insight. First, the probability of status change using Markov chain and second, forecasting by using VAR-NN-PSO. More details we classify by three classifications no risk (1-30), medium risk (30-48), and moderate (>49) in Pingtung and Chaozhou. This data is starting from January 2014 to May 2019 and it can be modeled with the Markov chain. At the same time, we perform Hybrid VAR-NN-PSO to forecast PM 2.5 in Pingtung and Chaozhou. In this optimization, the search for best solutions is carried out by a population consisting of several particles. Based on the results of the discussion, opportunities for the transition from monthly status change are obtained continuous stochastic time with a stationary probability distribution. Regarding the VAR-NN-PSO, we obtained the mean absolute percentage error (MAPE) 3.57% for PM 2.5 data in Pingtung and 4.87% for PM 2.5 data in Chaozhou, respectively. This model can be predicted to forecasting 180 days ahead. Besides, the population in PSO has generated randomly with the smallest value and the high value the accuracy.

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Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based

Cited by 6 publications

References 7 publications

National Vaccination and Local Intervention Impacts on COVID-19 Cases

National Vaccination and Local Intervention Impacts on COVID-19 Cases

Impacts of the Transformation to Industry 4.0 in the Manufacturing Sector: The Case of the U.S.

Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO

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