Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Nosratabadi, Saeed; Mosavi, Amir; Duan, Puhong; Ghamisi, Pedram; Ferdinand, Filip; Band, Shahab S.; Reuter, Uwe; Gama, João; Gandomi, Amir H.

doi:10.3390/math8101799

Cited by 105 publications

(42 citation statements)

References 93 publications

(138 reference statements)

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“…Similar results have been obtained. Similar results were concluded in different settings [13][14][15][16][17][18][19][20][21][22][23] when handling missing data using ML/AI techniques, depends on the scenario and type of the data structure to be imputed.…”

Section: Discussionsupporting

confidence: 72%

“…In the last decade, the use of artificial intelligence (AI) and machine-learning (ML) techniques has increased substantially in a diverse range of disciplines, such as climatology, industry, and biomedicine [13][14][15][16][17][18][19][20]. There is a vast and diverse number of ML algorithms which may be mostly categorized based on the approach taken toward the data set as well as the type of data processed.…”

Section: Introductionmentioning

confidence: 99%

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Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Larracoechea

Borges

2021

Mathematics

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Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBRAE) methods. The results varied depending on the missingness rate and mechanism. The i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance.

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Section: Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Larracoechea

Borges

2021

Mathematics

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“…To ensure that the greatest number of articles are found in the common field of food security and social capital, the present study used the Prisma method. The Prisma method is a systematic literature review method constituting four steps: (1) Identification, (2) screening, (3) eligibility, (4) inclusion [23,24]. These four steps are designed in such a way that researchers can find the most relevant articles to the subject under study.…”

Section: Methodsmentioning

confidence: 99%

Social Capital Contributions to Food Security: A Comprehensive Literature Review

Nosratabadi

Khazami

Abdallah

et al. 2020

Foods

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Social capital creates a synergy that benefits all members of a community. This review examines how social capital contributes to the food security of communities. A systematic literature review, based on Prisma, is designed to provide a state of the art review on capacity social capital in this realm. The output of this method led to finding 39 related articles. Studying these articles illustrates that social capital improves food security through two mechanisms of knowledge sharing and product sharing (i.e., sharing food products). It reveals that social capital through improving the food security pillars (i.e., food availability, food accessibility, food utilization, and food system stability) affects food security. In other words, the interaction among the community members results in sharing food products and information among community members, which facilitates food availability and access to food. There are many shreds of evidence in the literature that sharing food and food products among the community member decreases household food security and provides healthy nutrition to vulnerable families, and improves the food utilization pillar of food security. It is also disclosed that belonging to the social networks increases the community members’ resilience and decreases the community’s vulnerability that subsequently strengthens the stability of a food system. This study contributes to the common literature on food security and social capital by providing a conceptual model based on the literature. In addition to researchers, policymakers can use this study’s findings to provide solutions to address food insecurity problems.

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“…With the advent of the information age, DL methods play an important role in various fields of natural science research and have made outstanding contributions. Nosratabadi et al [42] pointed out that the algorithm of hybrid DL models will be further applied to various fields of data science, including the stock market, marketing, and cryptocurrency. With the extensive development of computer science and statistics, AI also promotes the application of DL in Earth science, hydrological processes, and climate change, giving the DL algorithm natural applicability in the application field of weather forecasting [43].…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

Peng

Cao

Liu

et al. 2021

Entropy

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The variation of polar vortex intensity is a significant factor affecting the atmospheric conditions and weather in the Northern Hemisphere (NH) and even the world. However, previous studies on the prediction of polar vortex intensity are insufficient. This paper establishes a deep learning (DL) model for multi-day and long-time intensity prediction of the polar vortex. Focusing on the winter period with the strongest polar vortex intensity, geopotential height (GPH) data of NCEP from 1948 to 2020 at 50 hPa are used to construct the dataset of polar vortex anomaly distribution images and polar vortex intensity time series. Then, we propose a new convolution neural network with long short-term memory based on Gaussian smoothing (GSCNN-LSTM) model which can not only accurately predict the variation characteristics of polar vortex intensity from day to day, but also can produce a skillful forecast for lead times of up to 20 days. Moreover, the innovative GSCNN-LSTM model has better stability and skillful correlation prediction than the traditional and some advanced spatiotemporal sequence prediction models. The accuracy of the model suggests important implications that DL methods have good applicability in forecasting the nonlinear system and vortex spatial–temporal characteristics variation in the atmosphere.

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Cited by 105 publications

References 93 publications

Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Social Capital Contributions to Food Security: A Comprehensive Literature Review

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

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