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Forecasting
Scopus
ISSN 2571-9394 · MDPI AG · 393 artikel di indeks
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Artikel Jurnal Teks lengkap
Machine Learning Forecasting of Strong Subsequent Events in New Zealand Using the NESTORE Algorithm
New Zealand, located along the boundary between the Pacific and Australian plates, is among the most seismically active regions in the world. In such an area, reliable short-term forecasting of strong aftershocks is essential for seismic risk mitigation. In this study, we apply NESTORE (NExt STrOng…
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The Impact of ESG Performance on Financial Performance: Evidence from Listed Companies in Thailand
Sustainable corporate governance plays an essential role in promoting responsible economic growth and enhancing social and environmental well-being in emerging economies. In this context, Environmental, Social, and Governance (ESG) performance has become an important indicator of a firm’s…
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Investigation of Sudden Stratospheric Warming (SSW) Events Between 1980 and 2100
The main objective of this work is to characterize Sudden Stratospheric Warming (SSW) conditions and their impact on local weather forecasting and climate change, using SSW definition criteria. The SSWs strongly affect Arctic vortex structure and midlatitude weather conditions. This work evaluates…
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Forecasting Municipal Financial Distress in South Africa: A Machine Learning Approach
Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a…
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Series-Core Fusion Based Multivariate Variational Mode Decomposition for Short-Term Wind Power Prediction Using Multiple Meteorological Data
Accurate wind power forecasting is critical for enhancing the operational efficiency and stability of electrical power grids. Conventional single-variable signal decomposition forecasting methods ignore the coupling relationship between wind power and multiple meteorological data, thus limiting…
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Satellite Data and Artificial Intelligence for FINtech
The SAIFIN project (Satellite data and Artificial Intelligence for FINtech) develops a novel algorithmic trading system that integrates satellite imagery, financial data, and advanced artificial intelligence to enhance decision-making, particularly in commodity and agricultural markets. This paper…
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An Explainable Voting Ensemble Framework for Early-Warning Forecasting of Corporate Financial Distress
Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework…
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Beyond Accuracy: The Cognitive Economy of Trust and Absorption in the Adoption of AI-Generated Forecasts
AI Recommender Systems (RecSys) function as personalised forecasting engines, predicting user preferences to reduce information overload. However, the efficacy of these systems is often bottlenecked by the “Last Mile” of forecasting: the end-user’s willingness to adopt and rely on the prediction…
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Multi-Scale Explainable AI for RMB Exchange Rate Drivers
To address the nonlinear nature of exchange rates where drivers vary by time horizon, this paper proposes a CEEMDAN-PE-CatBoost-SHAP framework. Analyzing USD/CNY data (2012–2024), we decomposed rates into high, medium, and low frequencies to bridge machine learning with economic interpretability…
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A Comparative Study of Univariate Models for Baltic Dry Index Forecasting
The Baltic Dry Index (BDI) measures the cost of transporting dry bulk commodities such as coal, iron ore, and grain. As a key indicator of global trade, supply chain dynamics, and overall economic activity, accurate short-term forecasting of the BDI is crucial. This paper compares six univariate…
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Projection of Changes in Coastal Water Temperature of the Baltic Sea up to 2100
Temperature is a fundamental property of water that determines its quality and the course of both biotic and physical processes. Therefore, the distribution and future changes in thermal conditions are crucial for the functioning of the hydrosphere. In this study, a hybrid air2water model was used…
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Climate Indices as Potential Predictors in Empirical Long-Range Meteorological Forecasting Models
Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional…
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New Statistical Approach to Forecasting Earth’s Skin Temperature from MERRA-2 Satellite Using Semiparametric Time Series Regression with Mixed Additive Spline Fourier (STSR-MASF)
We introduce the Semiparametric Time Series Regression with Mixed Additive Spline Fourier (STSR–MASF) model as an innovative approach for analyzing time series data with complex patterns. The model combines the flexibility of the spline estimator in capturing nonlinear variations across specific…
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A Highly Accurate and Efficient Statistical Framework for Short-Term Load Forecasting: A Case Study for Mexico
Short-term load forecasting is fundamental for the effective and reliable operation of power systems. Very accurate forecasting methods often involve complex hybrid approaches that combine statistical, physical, and/or intelligent techniques. In this work, we present an innovative, clear, and…
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Wind Shear Prediction at Jeju International Airport Using a Tree-Based Machine Learning Algorithm
This study employed tree-based machine learning (ML) algorithms to predict low-level wind shear (LLWS) at Jeju International Airport (ICAO: RKPC). Hourly meteorological data from 47 observation stations across Jeju Island, collected between 2019 and 2023, were split into training (60%), validation…
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Pollutant-Specific Deep Learning Architectures for Multi-Species Air Quality Bias Correction: Application to NO2 and PM10 in California
Accurate air quality forecasting remains challenging due to persistent biases in chemical transport models. Addressing this challenge, the current study develops pollutant-specific deep learning frameworks that correct systematic errors in the Community Multiscale Air Quality (CMAQ) simulations of…
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Learning Rare Events: Deep Learning Approaches to Extreme Price Prediction
Price spikes are rare but economically significant events observed across electricity, financial, commodity, and cryptocurrency markets. Their abrupt magnitude, heavy-tailed distributions, and severe class imbalance make them difficult to forecast using conventional time-series methods. This…
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Interactions Between Business Cycles, Financial Cycles and Monetary Policy in South Africa
This study set out to investigate the interactions between business cycles, financial cycles and monetary policy in South Africa. Explicitly, the study aims to examine the role of financial factors in business cycle models and the possibility of a unified macroeconomic framework in South Africa…
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Regime-Aware Stock Index Forecasting Under Latent Market States: A Hybrid Statistical Learning Framework with Cross-Market Validation
This study proposes a hybrid forecasting framework that integrates Kalman Filtering (KF), Markov Switching (MS), and nonlinear recurrent learning for stock-index prediction. The KF component smooths short-term price noise, the MS model identifies latent return–volatility regimes, and the LSTM/GRU…
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Forecasting South Africa’s Coal-to-Clean Energy Transition: A Monte Carlo Simulation
South Africa remains one of the world’s most coal-dependent electricity systems, with coal accounting for 81.57% of generation in 2023. Despite policy interventions to diversify the energy mix, structural change is slow to emerge. This study provides the first integrated, empirically calibrated…