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Journal articles on the topic 'Developing Economies Forecasting'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

González-Rivera, Gloria, Prakash Loungani, and Xuguang (Simon) Sheng. "Forecasting issues in developing economies." International Journal of Forecasting 35, no. 3 (2019): 927–28. http://dx.doi.org/10.1016/j.ijforecast.2019.04.005.

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2

Bhawnani, Vijay, and K. Rao Kadiyala. "Forecasting foreign exchange rates in developing economies." Applied Economics 29, no. 1 (1997): 51–62. http://dx.doi.org/10.1080/000368497327399.

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3

Chuku, Chuku, Anthony Simpasa, and Jacob Oduor. "Intelligent forecasting of economic growth for developing economies." International Economics 159 (October 2019): 74–93. http://dx.doi.org/10.1016/j.inteco.2019.06.001.

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Iskandar, Iskandar, Roger Willett, and Shuxiang Xu. "The development of a government cash forecasting model." Journal of Public Budgeting, Accounting & Financial Management 30, no. 4 (2018): 368–83. http://dx.doi.org/10.1108/jpbafm-04-2018-0036.

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Purpose Government cash forecasting is central to achieving effective government cash management but research in this area is scarce. The purpose of this paper is to address this shortcoming by developing a government cash forecasting model with an accuracy acceptable to the cash manager in emerging economies. Design/methodology/approach The paper follows “top-down” approach to develop a government cash forecasting model. It uses the Indonesian Government expenditure data from 2008 to 2015 as an illustration. The study utilises ARIMA, neural network and hybrid models to investigate the best pr

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Kaboudan, Mahmoud A. "Oil Revenue and Kuwait's Economy: An Econometric Approach." International Journal of Middle East Studies 20, no. 1 (1988): 45–66. http://dx.doi.org/10.1017/s0020743800057500.

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This paper presents a macroeconomic model for a small developing oil-exporting economy: Kuwait. The model is a simultaneous system of difference equations. Historic effects of changes in revenues from oil exports on the country's economic conditions are simulated. The model is then used to forecast these conditions through 1990, and to test two fiscal policy alternatives under the assumption that revenues from Kuwait's oil exports will remain constant from 1986 to 1990. The following are key words: developing economies; oil-exporting economies; Middle East economies; Kuwait; Kuwait's economy;

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6

Umoru, David, Solomon Edem Effiong, Salisu Shehu Umar, et al. "Forecasting exchange rate dynamics in developing countries." Corporate and Business Strategy Review 4, no. 2, special issue (2023): 238–50. http://dx.doi.org/10.22495/cbsrv4i2siart3.

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Given that volatility influences decisions about currency rates, monetary policy, and macroeconomic policy, it is crucial to predict and anticipate volatility in emerging economies. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) asymmetric models to estimate and forecast exchange rate dynamics in developing countries. We found that South Africa model had similar variance and covariance proportion of 0.99356 percent and 0.995901 percent respectively and the exchange rate could rise or fall by 2 to 6 units of rand, in exchange for USD. In Kenya, exchange rat

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PANKRATOV, E. L. "MODEL FOR PROGNOSIS OF ECONOMIC GROWTH: ACCOUNTING OF INFLUENCE OF THE ENVIRONMENT." Global Economy Journal 19, no. 01 (2019): 1950007. http://dx.doi.org/10.1142/s2194565919500076.

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In this paper, we introduce a model for forecasting economic growth. The introduced model describes the dynamics of developing economies that are under conditions of a strong impact of effects related to the state of the environment.

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Dong Thi Ngoc, Lan, Nguyen Dinh Hoan, and Ha-Nam Nguyen. "Gross Domestic Product Forecasting Using Deep Learning Models with a Phase-Adaptive Attention Mechanism." Electronics 14, no. 11 (2025): 2132. https://doi.org/10.3390/electronics14112132.

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Forecasting GDP is a highly practical task in macroeconomics, especially in the context of rapidly changing economic environments caused by both economic and non-economic factors. This study proposes a deep learning model that integrates Long Short-Term Memory (LSTM) networks with a phase-adaptive attention mechanism (PAA-LSTM model) to improve forecasting accuracy. The attention mechanism is flexibly adjusted according to different phases of the economic cycle—recession, recovery, expansion, and stagnation—allowing the model to better capture temporal dynamics compared to traditional static a

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Ibrahim, Barrie, Paul Agupugo Chijioke, Omoze Iguare Happy, and Folarin Abisade. "Leveraging machine learning to optimize renewable energy integration in developing economies." Global Journal of Engineering and Technology Advances 20, no. 3 (2024): 080–93. https://doi.org/10.5281/zenodo.14921953.

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The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing reso

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Henry, James, and Amade Peter. "Application of Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Electricity Consumption for Nigeria." Journal of Environmental and Energy Economics 2, no. 1 (2024): 13–21. https://doi.org/10.56946/jeee.v2i1.311.

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The electricity demand relative to supply has increased in the developed and developing economies. Hence, forecasting adequate electricity supply plays a crucial role in driving economic production activities and growth. This is so because electricity supply in relation to demand is vital for future energy planning and policy formulation. Thus, this study employed the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model and data on electricity consumption from 1971 to 2014. The result indicates that the ARIMA (1, 1, 2) model is adequate for forecasting and modelling electricity c

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CHIMELI, ARIASTER B., FRANCISCO DE ASSIS DE SOUZA FILHO, MARCOS COSTA HOLANDA, and FRANCIS CARLO PETTERINI. "Forecasting the impacts of climate variability: lessons from the rainfed corn market in Ceará, Brazil." Environment and Development Economics 13, no. 2 (2008): 201–27. http://dx.doi.org/10.1017/s1355770x07004172.

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ABSTRACTA number of studies show that climatic shocks have significant economic impacts in several regions of the world, especially in, but not limited to, developing economies. In this paper we focus on a drought-related indicator of well-being and emergency spending in the Brazilian semi-arid zone – rainfed corn market – and estimate aggregate behavioral and forecast models for this market conditional on local climate determinants. We find encouraging evidence that our approach can help policy makers buy time to help them prepare for drought mitigating actions. The analysis is applicable to

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12

Soni, Neha. "Impact of AI and Machine Learning on Supply Chain Optimization in Developing Economies." Journal of Technology Management for Growing Economies 14, no. 2 (2023): 1–7. https://doi.org/10.15415/jtmge/2023.142001.

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Background: Emerging economies face various unsolved issues that limit supply chain development, such as inefficiency and unhealthy competition, lack of transparency, and an underdeveloped technological infrastructure. Purpose: This paper describes how Artificial Intelligence and Machine Learning can solve these problems with the help of supply chain management in various areas. In developing economies, Artificial Intelligence and Machine Learning are transforming supply chain management, offering exceptional opportunities for optimization. In this paper, the innovative potential of AI and Ml

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Klinov, V. "Great Reshuffle of the World Economy (On the Book by A. A. Akaev "From the Epoch of Great Divergence to the Epoch of Great Convergence")." Voprosy Ekonomiki, no. 11 (November 20, 2015): 144–55. http://dx.doi.org/10.32609/0042-8736-2015-11-144-155.

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A dramatic shift in the balance of economic power among advanced and developing countries is demonstrated by comparing GDP of the largest economies. The world economy prospects are also evaluated up to the middle of the current century. Major ways of economic growth models building are considered. Problems of long waves of economic development concept application for forecasting advances in technology and GDP growth per capita have been outlined.

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Oyelam, Abiodun, and Rotimi Ogundeji. "Bayesian seasonal autoregressive integrated moving average: Modelling two decades of inflation dynamics in Nigeria." Science World Journal 20, no. 1 (2025): 56–63. https://doi.org/10.4314/swj.v20i1.8.

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There is a growing recognition of the unique characteristics of individual economies, particularly in developing countries. These peculiarities necessitate country-specific studies, as they provide insights tailored to the economic realities and challenges of the nation under examination. This study was motivated by the need to explore these specific dynamics. This study employed Bayesian statistical methods to model and forecast inflationary dynamics in Nigeria over two decades (January 2003 to September 2024). Adopting a Bayesian Seasonal Autoregressive Integrated Moving Average, (SARIMA) fr

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Schroeder de Witt, Christian, Catherine Tong, Valentina Zantedeschi, et al. "RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 14902–10. http://dx.doi.org/10.1609/aaai.v35i17.17749.

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Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce RainBench, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of releva

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Ibrahim Barrie, Chijioke Paul Agupugo, Happy Omoze Iguare, and Abisade Folarin. "Leveraging machine learning to optimize renewable energy integration in developing economies." Global Journal of Engineering and Technology Advances 20, no. 3 (2024): 080–93. http://dx.doi.org/10.30574/gjeta.2024.20.3.0170.

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The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing reso

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17

Tleppayev, Arsen, and Saule Zeinolla. "Forecasting Youth Unemployment Through Educational and Demographic Indicators: A Panel Time-Series Approach." Forecasting 7, no. 3 (2025): 37. https://doi.org/10.3390/forecast7030037.

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Youth unemployment remains a pressing issue in many emerging economies, where educational disparities and demographic pressures interact in complex ways. This study investigates the links between higher-education enrolment, demographic structure and youth unemployment in eight developing countries from 2009 to 2023. Panel cointegration techniques—Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS)—are applied to estimate the long-run effects of gross tertiary-school enrolment on youth unemployment while controlling for GDP growth and youth-cohort size. Robus

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Honcharov, Yu V., M. V. Dykha, V. Voronina, A. Milka, and N. Klymenchukova. "Forecasting the innovation of Ukraine’s economic development in a global dimension." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 1 (February 28, 2023): 174–81. http://dx.doi.org/10.33271/nvngu/2023-1/174.

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Purpose. To analyze the state and trends of innovative development of the countries of the world, to define the place of Ukraine in the international arena and forecast its innovation. Methodology. The methodological basis was formed by general scientific theoretical methods: generalization, explanation, grouping; statistical method (for a comprehensive assessment of the state and trends of innovative activity of the economies of the countries of the world and Ukraine); methods of analysis and synthesis, economic and mathematical methods of forecasting, methods of abstraction and visualization

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Helhel, Yesim, and Seref Kalayci. "Exchange Rate Forecasting Based on Fundamental Macroeconomic Variables in a Floating Exchange Rate Regime." International Journal of Social Ecology and Sustainable Development 3, no. 3 (2012): 15–21. http://dx.doi.org/10.4018/jsesd.2012070102.

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Developing countries had a fixed exchange rate regime and avoided financial liberalization until the 1990’s. In the early 2000’s however, most of the developing countries abandoned their fixed exchange rate regimes in favor of floating rate regimes which in turn increased the importance of exchange rate forecasting in the emerging market economies. This paper intends to explain TR/USD (Turkish Lira/American Dollar) exchange rates by using macroeconomic fundamentals for the period between February 2001 and December 2009 on a monthly basis. A Vector Auto Regression (VAR) method is used. Among th

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Chavula, Petros, Fredrick Kayusi, and Bismark Agura Kayus. "Linking New Information Technologies to Agricultural Economics: The Role of Artificial Intelligence Integration." LatIA 3 (April 16, 2025): 326. https://doi.org/10.62486/latia2025326.

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Artificial Intelligence (AI) is revolutionizing agricultural economics by optimizing productivity, reducing costs, and enhancing decision-making processes. This paper explores the integration of AI technologies—such as machine learning, predictive analytics, and automation—into agricultural economic frameworks. AI-driven innovations, including precision farming, yield forecasting, and supply chain management, are reshaping agricultural practices by improving efficiency and sustainability. Furthermore, AI facilitates data-driven policymaking, enabling governments and stakeholders to address foo

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Bandara, W. M. S., and W. A. R. De Mel. "Evaluating the Predictive Performance of Monthly Inflation Rates in Sri Lanka using the Hybrid Model (HB)." Asian Journal of Probability and Statistics 25, no. 4 (2023): 1–14. http://dx.doi.org/10.9734/ajpas/2023/v25i4568.

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Aims/ objectives: This study develops and evaluates a novel hybrid model (HB) for forecasting monthly inflation rates in Sri Lanka, a country with a unique economic context, from 1988 to 2021. By integrating the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs), the study aims to overcome the limitations of traditional linear models in capturing the nonlinear patterns often observed in Sri Lankan economic data. Objectives: The study aims to assess the predictive accuracy of the HB model against established models, emphasizing its adaptability and robustness

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Anand, Atul, and L. Suganthi. "Forecasting of Electricity Demand by Hybrid ANN-PSO Models." International Journal of Energy Optimization and Engineering 6, no. 4 (2017): 66–83. http://dx.doi.org/10.4018/ijeoe.2017100105.

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Developing economies need to invest in energy projects. Because the gestation period of the electric projects is high, it is of paramount importance to accurately forecast the energy requirements. In the present paper, the future energy demand of the state of Tamil Nadu in India, is forecasted using an artificial neural network (ANN) optimized by particle swarm optimization (PSO) and by General Algorithm (GA). Hybrid ANN Models have the potential to provide forecasts that perform well compared to the more traditional modelling approaches. The forecasted results obtained using the hybrid ANN-PS

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Liang, Yao, Xu Jin, and Aslan Javid Azimzadeh. "INTERCONNECTIONS AND INTERDEPENDENCIES OF ECONOMIC DEVELOPMENT AND SHADOW BANKING SECTOR IN DEVELOPING AND TRANSITIONAL ECONOMIES." Technological and Economic Development of Economy 30, no. 5 (2024): 1392–411. http://dx.doi.org/10.3846/tede.2024.20795.

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The research objective is defined as the identification and confirmation of empirical relationships between shadow banking activities and economic development in developing and transitional economies to establish a theoretical basis for minimizing potential risks associated with shadow banking. The methodological design is based on a quantitative approach, implemented through correlation-regression analysis and ARIMA forecasting methods. The research findings confirm Hypothesis 1: China’s shadow banking is closely interconnected with the country’s economic development. However, Hypothesis 2 (t

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Ramírez García, Abraham, and Ana Lorena Jiménez Preciado. "Covid-19 and Economics Forecasting on Advanced andEmerging Countries." EconoQuantum 18, no. 1 (2020): 21–43. http://dx.doi.org/10.18381/eq.v18i1.7222.

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Objective: To estimate the size and the dynamics of the coro-navirus (covid-19) pandemic in Advanced, Emerging, and Developing Economies, and to determine its implications for economic growth.Methodology: A susceptible Infected Recovered (sir) mod-el is implemented, we calculate the size of the pandemic through numerical integration and phase diagrams for covid-19 trajectory; finally, we use ensemble models (ran-dom forest) to forecast economic growth.Results: We confirm that there are differences in pandemic spread and size among countries; likewise, the trajectories show a long-term spiral c

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Khan, Atif Maqbool, and Magdalena Osińska. "How to Predict Energy Consumption in BRICS Countries?" Energies 14, no. 10 (2021): 2749. http://dx.doi.org/10.3390/en14102749.

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Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contrib

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Bissessur, Sheik Ahmad Ryman, and Noor-Ul-Hacq Sookia. "Assessing Economic Activities by Using Term Spreads." International Journal of Economics, Management and Accounting 31, no. 2 (2023): 417–37. http://dx.doi.org/10.31436/ijema.v31i2.1116.

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Both linear and non-linear models have been used in literature in developed countries to assess the forecasting power of the term spread on output. When it comes to developing economies, however, a gap in research exists. As such, the main objective of this research work is to assess the predicting capacity of the variable term spread on economic growth in the context of a developing country, namely, Mauritius. In this regard, an extended production function is designed that includes major relevant macroeconomic variables such as Spread, Investment, Human Capital, Openness to Trade, Foreign Di

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Akhmedov, Khasan. "Financial Planning and Corporate Development: Attitude Analysis of Selected Enterprises in Uzbekistan." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 3, no. 6 (2017): 38–42. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.36.1005.

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Financial planning has become an integral part of corporate development strategy in businesses in developed and emerging economies. Despite the rapid spread of advanced business practices, enterprises in transition and developing economies still have been lagging behind in absorbing some elements of modern business conduct skills and methods. This weakness is visible in corporate planning and forecasting practices. Although key elements of the market economy have already been introduced and a business environment is functioning favorably, enterprises in transition economies of the post-Soviet

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Kolesov, R. V., A. J. Tarasova, and G. E. Kozlov. "The Inflation Level Forecasting in Forming Regional and Municipal Budgets." Economics, taxes & law 12, no. 4 (2019): 102–10. http://dx.doi.org/10.26794/1999-849x-2019-12-4-102-110.

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The subject of the research is inflation level forecasting in forming, executing and analyzing the execution course of regional and municipal budgets. The purpose of the work is developing methods of inflation level evaluation to supply state and local authorities with necessary and sufficient operational data reflecting the specificity of respective regional economies concerning prognostic figures of monthly and yearly rates of inflation. The urgency of the research lies in the fact that the proposed approach to inflation level evaluation is relatively simple but at the same time effective an

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Jiang, Yulian, Wuchang Wei, Ramesh Chandra Das, and Tonmoy Chatterjee. "Analysis of the Strategic Emission-Based Energy Policies of Developing and Developed Economies with Twin Prediction Model." Complexity 2020 (November 11, 2020): 1–16. http://dx.doi.org/10.1155/2020/4701678.

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Upholding sustainability in the use of energies for the increasing global industrial activity has been one of the priority agendas of the global leaders of the West and East. The projection of different GHGs has thus been the important policy agenda of the economies to justify the positions of their own as well as of others. Methane is one of the important components of GHGs, and its main sources of generation are the agriculture and livestock activities. Global diplomacy regarding the curtailment of the GHGs has set the target of reducing the levels of GHGs time to time, but the ground realit

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Kurzenev, Vladimir A., and Vladimir T. Perekrest. "Balance Technologies for Calculating and Predictive Estimation of Labor Resources for Regional Socio-Economic Systems." Administrative consulting, no. 4 (160) (June 7, 2022): 35–43. https://doi.org/10.22394/1726-1139-2022-4-35-43.

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The article analyzes the institutional and technological features of the calculus (representation) and predictive assessment of the balance of labor resources for regional economic systems. The review is carried out in the general context of the particular tasks of developing egional strategies: goal-setting, analysis and forecasting. The theoretical and practical experience of the laboratory of mathematical methods of data analysis of IPRE RAS and St. Petersburg EMI RAS (until 2018) in the framework of the direction “Economic and mathematical methods for state regulation of socially ori

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Abdel-Baki, Monal, Alexander Kostyuk, and Dmitriy Govorun. "Will the proposed regulatory reforms by the Basel committee improve economic performance in emerging economies? An empirical application to Egypt and Ukraine." Corporate Ownership and Control 8, no. 2 (2011): 14–29. http://dx.doi.org/10.22495/cocv8si1p2.

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The aim of this research is to assess the efficacy of the prospective reforms proposed by the Basel Committee on emerging market economies. Egypt and Ukraine are selected as comparative case studies representing middle-income developing nations and transition economies that have shown diverse reactions to the global crisis. Using a small-scale DSGE model, the projected changes to capital adequacy measures, minimum liquidity requirements and Corporate Governance are tested on a set of macroeconomic outputs: GDP growth, employment, inflation and interest rates over the period of 2000:01-2010:03.

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Karabachinskiy, A. L., G. N. Mezentsev, and S. I. Tseluyko. "THE REGION OF EXISTENCE AND MATHEMATICAL MODEL TAX-FREE ECONOMY." Bulletin of the Tver State Technical University. Series «Social Sciences and Humanities», no. 3 (2020): 89–113. http://dx.doi.org/10.46573/2409-1391-2020-3-89-113.

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The article raises the theoretical problem of determining the possible sustainable functioning of the economy in the emission formation of budget revenues in compliance with the conditions of the absence of inflation or its availability within controlled limits. Describes the historical conditionality of occurrence of the tax economy, the interaction between the processes of circulation of money and commodity masses, but also the inefficiency of the tax system in the conditions of monetary circulation. The possibility of an evolutionary transition to the tax-exempt economy in the regime of con

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Škare, Marinko, and Saša Stjepanović. "HOW IMPORTANT ARE GENERAL EQUILIBRIUM MODELS FOR SMALL OPEN ECONOMIES – A CASE OF CROATIA." Technological and Economic Development of Economy 19, no. 2 (2013): 331–49. http://dx.doi.org/10.3846/20294913.2013.799612.

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This paper develops a general equilibrium model for the Republic of Croatia to evaluate CGE model use in macroeconomic management and forecasting. Since Croatia is a small open country subject to large external shock and growth constraints efficient macroeconomic management framework is fundamental. The lack of data prevents to follow historic economic variables over a longer period of time, which is why CGE models look as possible solution. CGE models use calibration to solve the problem of missing most macroeconomic variables. After developing the model, we use it to run different scenarios

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Syed, Mohd Khalid, and Babli Dhiman Dr. "Co-integration of National Stock Exchange India with Global Stock Markets: An Empirical Analysis." Indian Journal of Economics and Business 20, no. 1 (2021): 1115–23. https://doi.org/10.5281/zenodo.7445882.

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Stock market investments strategies are always complex and extremely tough to understand as the quantum of data is very high. In the recent years so many methods have been used to evaluate and understand various phenomenon’s of the market but the machine learning techniques have been mostly used for understanding the dynamics of market and forecasting the market prices. The goal of this paper is to examine how the Indian stock market (NSE) interacts with other global stock markets, including those of industrialized nations (the USAand Japan) and emerging nations (China, Indonesia) From J

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Afanasiev, Mikhail. "Economic complexity and embedding of regional economies’ structures." Economics and the Mathematical Methods 57, no. 3 (2021): 67. http://dx.doi.org/10.31857/s042473880016410-0.

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The research focuses on the development of localized specialization and economic diversification theories. Our task is forecasting of the emergence of new strong sectors in the region. On the basis of probabilistic and statistical modeling the model which allows estimating the probability of appearing a new strong sector in the region taking into account characteristics of economic structure is constructed. The possibility of building such a model is based on the assumption that the emergence and development of sectors is largely determined by the evolution of past economic activity. The model

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Tudor, Cristiana, and Robert Sova. "Benchmarking GHG Emissions Forecasting Models for Global Climate Policy." Electronics 10, no. 24 (2021): 3149. http://dx.doi.org/10.3390/electronics10243149.

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Climate change and pollution fighting have become prominent global concerns in the twenty-first century. In this context, accurate estimates for polluting emissions and their evolution are critical for robust policy-making processes and ultimately for solving stringent global climate challenges. As such, the primary objective of this study is to produce more accurate forecasts of greenhouse gas (GHG) emissions. This in turn contributes to the timely evaluation of the progress achieved towards meeting global climate goals set by international agendas and also acts as an early-warning system. We

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Abhishek, Banerjee. "AI for Smart Irrigation Using IoT and Weather Forecasting." Journal of Research and Innovation in Technology, Commerce and Management Vol. 2, Issue 6 (2025): 2622–29. https://doi.org/10.5281/zenodo.15573442.

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This research explores the integration of Artificial Intelligence (AI), Internet of Things (IoT), and weather forecasting in smart irrigation systems to optimize water usage in agriculture. Conducted using a descriptive qualitative approach, the study evaluates how AI-based irrigation solutions can mitigate water scarcity, improve crop productivity, and reduce human intervention. The use of sensor-driven data combined with realtime weather predictions allows intelligent systems to determine the right quantity and timing for irrigation. This paper examines the implementation of smart irrigation

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Nurlanuly, Abdul-Khassen, Nazym Akhmetzhanova, Oxana Kirichok, Ardakh Azimkhan, and Nailya Abdildinova. "Assessment of the air transportation market in the economies of newly independent states and forecast of its development." International Journal of Innovative Research and Scientific Studies 8, no. 3 (2025): 3843–51. https://doi.org/10.53894/ijirss.v8i3.7396.

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The air transportation market in the newly independent states is a dynamically developing sector that significantly influences economic growth and the integration of these countries into the global economy. The aim of this study is to assess the relationship between air transportation indicators and economic indicators, including GDP per capita. A comparative analysis was conducted across three periods: pre-pandemic (2016–2019), during the COVID-19 pandemic (2020–2021), and post-pandemic (2022–2024), using correlation and regression analysis, as well as the ARIMA time series forecasting model.

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Mohamed, Maha Mohamed Alsebai, Pingfeng Liu, and Guihua Nie. "Do Knowledge Economy Indicators Affect Economic Growth? Evidence from Developing Countries." Sustainability 14, no. 8 (2022): 4774. http://dx.doi.org/10.3390/su14084774.

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The competitiveness of national economies is increasingly dependent on their ability to produce and use knowledge, as knowledge, education, and innovation are the main indicators of economic growth in a globalizing world. Many countries have adopted policies related to the production of knowledge and its transformation into wealth that stimulates the growth and competitiveness of their economies. Through our study, we measured some knowledge economy (KE) variables for a sample of 20 developing countries. During the period (1996–2020), using panel data, the estimate was made using three models:

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Poncela, Pilar, Eva Senra, and Lya Paola Sierra. "Global vs Sectoral Factors and the Impact of the Financialization in Commodity Price Changes." Open Economies Review 31, no. 4 (2020): 859–79. http://dx.doi.org/10.1007/s11079-019-09564-4.

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Abstract Commodity prices influence price levels of a broad range of goods and, in the case of some developing economies, production and export activity. Therefore, information about future commodity inflation is useful for central banks, forward-looking policy-makers, and economic agents whose decisions depend on their expectations about it. After 2004, we have witnessed the so-called financialization of the commodity markets, which might induce greater communalities among commodity prices. This paper reports evidence on the relevance of the forecasting content of co-movement after 2004. With

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Ardakani, F. J., and M. M. Ardehali. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types." Energy 65 (February 2014): 452–61. http://dx.doi.org/10.1016/j.energy.2013.12.031.

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Sova, Y. S., and T. V. Tokarchuk. "Special Aspects of Money and Stock Market Indicators Forecasting, Considering the Macroeconomic Shocks in Developing Countries." Business Inform 11, no. 526 (2021): 348–56. http://dx.doi.org/10.32983/2222-4459-2021-11-348-356.

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The purpose of the article is to improve approaches to forecasting and analyzing the sensitivity of money-and-credit and stock markets to macroeconomic shocks in developing countries, which are characterized by a high level of socio-economic uncertainty and political risks. The article substantiates the advantages of expanding the standard forecasting methodology based on one-sided forecasts by analyzing the sensitivity of the indicators studied to macroeconomic perturbations, especially in the period of high uncertainty observed in recent years. An empirical analysis of the reaction of the ma

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Chen, Xuanmo. "Predict the Inflation of Food Prices based on the Linear Regression Models." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 351–55. http://dx.doi.org/10.54097/ajj8fx20.

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Using the large "Global Food Price Inflation" dataset from Kaggle, this paper provides a thorough analysis of food price inflation in Cameroon. In the framework of Cameroon's dynamic economy, the research focuses on comprehending the complex financial indicators and their relationship to trends in food prices between 2015 and 2017. The study addresses the crucial role that agriculture plays in Cameroon's economy by using both polynomial and linear regression models to analyze and forecast food inflation rates. Because of its simple methodology, the linear regression model works better for maki

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Rahman, Habeebur, Iniyan Selvarasan, and Jahitha Begum A. "Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach." Energies 11, no. 12 (2018): 3442. http://dx.doi.org/10.3390/en11123442.

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Continual energy availability is one of the prime inputs requisite for the persistent growth of any country. This becomes even more important for a country like India, which is one of the rapidly developing economies. Therefore electrical energy’s short-term demand forecasting is an essential step in the process of energy planning. The intent of this article is to predict the Total Electricity Consumption (TEC) in industry, agriculture, domestic, commercial, traction railways and other sectors of India for 2030. The methodology includes the familiar black-box approaches for forecasting namely

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Pattnaik, Sarthak, Maryan Rizinski, and Eugene Pinsky. "Rethinking Inequality: The Complex Dynamics Beyond the Kuznets Curve." Data 10, no. 6 (2025): 88. https://doi.org/10.3390/data10060088.

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Income inequality has emerged as a defining challenge of our time, particularly in advanced economies, where the gap between rich and poor has reached unprecedented levels. This study analyzes income inequality trends from 2000 to 2023 across developed countries (the United States, the United Kingdom, Germany, and France) and developing nations using World Bank Gini coefficient data. We employ comprehensive visualization techniques, Pareto distribution analysis, and ARIMA time-series forecasting models to evaluate the effectiveness of the Kuznets curve as a predictor of income inequality. Our

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Wilson, W. Douglas, Scott Glenn, Travis Miles, Anthony Knap, and Cesar Toro. "Transformative Ocean Observing for Hurricane Forecasting, Readiness, and Response in the Caribbean Tropical Storm Corridor." Marine Technology Society Journal 55, no. 3 (2021): 90–91. http://dx.doi.org/10.4031/mtsj.55.3.43.

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Abstract The upper ocean in the Western Tropical Atlantic tropical storm corridor—including the Caribbean Sea—is under-sampled and climatologically warming (Figure 1). Regionally varying Essential Ocean Features impacting tropical cyclone dynamics include fresh water upper ocean layers, mesoscale eddies, high Upper Ocean Heat Content values, and inflows from the Subtropical and Equatorial Atlantic. Ongoing research indicates that hurricane intensity forecasts can be improved with expanded and sustained ocean data collection and utilization along the hurricane path.This proposed activity will b

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Lukong, Terence Kibula, Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatietse, and Detlef Schulz. "A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach." Energies 18, no. 10 (2025): 2484. https://doi.org/10.3390/en18102484.

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Maintaining a balance between electricity generation and consumption is vital for ensuring grid stability and preventing disruptions. Spatial load forecasting (SLF) predicts geographical electricity demand, thereby aiding in power system planning. However, conventional methods like multiple linear regression and autoregressive integrated moving average struggle to capture the complex spatiotemporal patterns in historical data. Advanced methods like spatiotemporal graph transformers, graph convolutional networks, and improved scale-limited dynamic time warping better capture these dependencies,

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Subramanian, Anishalache, Naveen Palanichamy, Kok-Why Ng, and Sandhya Aneja. "Climate Change Analysis in Malaysia Using Machine Learning." Journal of Informatics and Web Engineering 4, no. 1 (2025): 307–19. https://doi.org/10.33093/jiwe.2025.4.1.22.

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Climate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using a

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Chupin, Alexander, Dmitry Morkovkin, Marina Bolsunovskaya, Anna Boyko, and Alexander Leksashov. "Techno-Economic Sustainability Potential of Large-Scale Systems: Forecasting Intermodal Freight Transportation Volumes." Sustainability 16, no. 3 (2024): 1265. http://dx.doi.org/10.3390/su16031265.

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The sustainability of large economies is one of the most important challenges in today’s world. As the world strives to create a greener and more efficient future, it becomes necessary to accurately analyze and forecast freight volumes. By developing a reliable freight transportation forecasting model, the authors will be able to gain valuable insights into the trends and patterns that determine the development of economic systems. This will enable informed decisions on resource allocation, infrastructure development, and environmental impact mitigation. Such a model takes into account various

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Pilko, A. D., and V. R. Kramar. "The Impact of Monetary Policy on the Financial Stability of the Banking System: Setting a Modeling Problem." Business Inform 2, no. 517 (2021): 81–88. http://dx.doi.org/10.32983/2222-4459-2021-2-81-88.

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The publication is concerned with highlighting the results of the carried out analysis of the existing practice of developing macroeconomic models directed towards determining the main parameters of monetary policy of central banks, as well as assessing their impact on the indicators of financial stability of the banking system. Given the low efficiency of the traditional approaches to the formation of the monetary rule both in countries with developed market economies and in countries with small open economies (in particular, Taylor rule), possible ways to solve this problem are proposed taki

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