Data Science in Economics

Nosratabadi, Saeed; Mosavi, Amir; Duan, Puhong; Ghamisi, Pedram

doi:10.21203/rs.3.rs-27318/v1

Cited by 9 publications

(12 citation statements)

References 74 publications

(136 reference statements)

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“…An increase in imports is predicted to fulfil the demand. This is both economically and environmentally undesirable [16]. Blends were tested to offer a solution, but no satisfactory result was achieved.…”

Section: Results Of Individual Studiesmentioning

confidence: 99%

“…This research is based upon the assessment structure of the PRISMA statement presented in the Appendix A [3]. This reporting standard is widely used in the medical and health-care fields [13,14], and it is commonly accepted as a useful reporting guideline in those disciplines in order to enhance the completeness of the reporting of systematic reviews [15,16]. Numerous extensions have been added since the year it was published, to enable the reporting of different types of systematic reviews.…”

Section: Methodsmentioning

confidence: 99%

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PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector

et al. 2020

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The bioethanol sector is an extremely complex set of actors, technologies and market structures, influenced simultaneously by different natural, economic, social and political processes. That is why it lends itself to the application of system dynamics modelling. In last five years a relatively high level of experience and knowledge has accumulated related to the application of computer-aided system modelling for the analysis and forecasting of the bioethanol sector. The goal of the current paper is to offer a systematic review of the application of system dynamics models in order to better understand the structure, conduct and performance of the bioethanol sector. Our method has been the preferred reporting items for systematic reviews and meta-analyses (PRISMA), based on English-language materials published between 2015 and 2020. The results highlight that system dynamic models have become more and more complex, but as a consequence of the improvement in information technology and statistical systems, as well as the increasing experience gained they offer an efficient tool for decision makers in the business and political spheres. In the future, the combination of traditional system dynamics modelling and agent-based models will offer new perspectives for the preparation of more sophisticated description and forecasting.

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Section: Results Of Individual Studiesmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector

et al. 2020

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“…Data normalization refers to rescaling actual numeric features into a 0 to 1 range and is employed in machine learning to create a training model less sensitive to the scale of variables. Table 1 indicates all the technical indicators, which are employed as input values based on domain experts and previous studies [ 47 , 48 , 49 ]; the input values for calculating indicators in the table are opening, high, low and closing prices in each trading day; “t” means current time, and “t + 1” and “t − 1” mean one day ahead and one day before, respectively. Table 2 shows the summary statistics of indicators for the groups.…”

Section: Research Datamentioning

confidence: 99%

Deep Learning for Stock Market Prediction

Nabipour

Nayyeri

Jabani

et al. 2020

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255

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The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

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“…This paper contributes to the advancement of time-series modelling and prediction of COVID-19. Although ML has long been established as a standard tool for modeling natural disasters and weather forecasting [61][62][63][64][65], its application in modeling outbreak is still in the early stages. More sophisticated ML methods are yet to be explored.…”

Section: = − =mentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

et al. 2020

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Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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Data Science in Economics

Cited by 9 publications

References 74 publications

PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector

PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector

Deep Learning for Stock Market Prediction

COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

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