Big Data‐Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0

Liu, Jie

doi:10.1155/2021/3662204

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Liu [95] introduced a big-data-driven macroeconomic forecasting model and analyzed psychological decision behavior in the context of Industry 4.0. The paper emphasized the significance of big data technology in providing multilevel, diversified, and comprehensive information for economic forecasting.…”

Section: Focus Of Research Impact On Industrial Applicationsmentioning

confidence: 99%

Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements

Kashpruk,

Piskor-Ignatowicz,

Baranowski

2023

Applied Sciences

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Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0), offering a crucial analytical tool for the vast data streams generated by modern industrial processes. This literature review systematically consolidates existing research on the predictive analysis of time series within the framework of Industry 4.0, illustrating its critical role in enhancing operational foresight and strategic planning. Tracing the evolution from the first to the fourth industrial revolution, the paper delineates how each phase has incrementally set the stage for today’s data-centric manufacturing paradigms. It critically examines how emergent technologies such as the Internet of things (IoT), artificial intelligence (AI), cloud computing, and big data analytics converge in the context of Industry 4.0 to transform time series data into actionable insights. Specifically, the review explores applications in predictive maintenance, production optimization, sales forecasting, and anomaly detection, underscoring the transformative impact of accurate time series forecasting on industrial operations. The paper culminates in a call to action for the strategic dissemination and management of these technologies, proposing a pathway for leveraging time series prediction to drive societal and economic advancement. Serving as a foundational compendium, this article aims to inform and guide ongoing research and practice at the intersection of time series prediction and Industry 4.0.

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Section: Focus Of Research Impact On Industrial Applicationsmentioning

confidence: 99%

Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements

Kashpruk,

Piskor-Ignatowicz,

Baranowski

2023

Applied Sciences

View full text Add to dashboard Cite

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“…The overarching goal is to offer timely, insightful, and accurate qualitative and quantitative insights into the objective state of social and economic activities. By enhancing public comprehension of the inherent dynamics of human economic endeavors, this approach aids in discerning economic phenomena and forecasting future industry developments [1], [2], [3], [4], [5]. The significance of forecasting macroeconomic trends cannot be overstated, as it underpins the sustained, stable, and efficient functioning of the national economy while bolstering policy decision-making [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Establishment of Economic Analysis Model Based on Artificial Intelligence Technology

Shi

2024

ijacsa

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With the continuous evolution of artificial intelligence technology, its integration into economic analysis models is becoming increasingly prevalent. This paper employs the Lasso Back Propagation neural network method to conduct financial analysis and prediction for major global economies, focusing on total Gross Domestic Product, combined Gross Domestic Product growth rate, and Consumer Price Index. The real Gross Domestic Product of the top 30 countries in the global ranking is meticulously analyzed and categorized into various economic types. This categorization, coupled with the utilization of neural network multi-hidden layer variable analysis, facilitates the analysis and prediction of national economic trends. The findings reveal that overall economic growth among the top 30 countries is sluggish, albeit showing a growth trajectory. However, the driving force for economic growth remains notably inadequate. Moreover, employing a single time series model effectively predicts Gross Domestic Product and Consumer Price Index growth rates, alongside other macroeconomic indicators. Notably, the absence of autocorrelation in the fitting residual series underscores the applicability of the time series method for combined forecasting, affirming the robustness of the predictive framework.

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“…The main reason is that although the government has stabilized the economic growth through large-scale stimulus policies, it has also hindered the process of natural economic adjustment, leading to the artificially prolonged market economic cycle. In addition, considering the current decline in the return on capital of the real economy, the heavy financial burden, and the expectation of RMB devaluation, various uncertainties have seriously affected the economic recovery [4][5].…”

Section: Introductionmentioning

confidence: 99%

Research on Macro-economic Decision Model Based on Big Data Analysis

Yan

2022

SHS Web Conf.

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Macroeconomic forecasting is guided by rational expectation, which emphasizes various historical information used. It is similar to using historical data to predict macroeconomic development trends. When making investment decisions, enterprises generally need to consider the cost of investment. The economic downturn seems to have entered a bottomless pit. The main reason is that although the government has stabilized the economic growth through large-scale stimulus policies, it also hinders the process of economic natural adjustment, leading to the artificially prolonged market economic cycle. Therefore, in order to effectively deal with this complex situation, we must rely on a more mature theory and method system to systematically analyze the transmission mechanism that affects enterprise investment decisions. This paper will analyze the macro-economy based on big data. The development strategy of big data has become a hot issue for enterprises around the world. Many enterprises integrate big data strategy with their own decisionmaking management system, and make more scientific decisions through comprehensive collection, screening and analysis of all kinds of data.

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Big Data‐Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0

Cited by 4 publications

References 17 publications

Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements

Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements

Establishment of Economic Analysis Model Based on Artificial Intelligence Technology

Research on Macro-economic Decision Model Based on Big Data Analysis

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