Intermittent demand forecasting is crucial for firms and commercial activities. Recently, many researchers have focused on forecasting methods for intermittent demand and proposed various forecasting techniques. The most prominent methods among these proposed techniques are the Croston method, which is based on exponential smoothing, and its two popular variations: SBA (Syntetos-Boylan Approximation), SBJ (Shale-Boylan-Johnston Approximation). Croston method is widely used in forecasting of intermittent demand and inventory (stock) control. Since these demands usually include zero values, using the ground breaking method developed by Croston in this data becomes inevitable. Nevertheless, there are some shortcomings to this method such as producing biased forecasts and for this reason its variations have been proposed. ATA method is a recently developed forecasting method which is an alternative to exponential smoothing. In this paper we propose a modification of ATA method that can be used for forecasting of intermittent demand. We will compare the results of the proposed approach to those of Croston and other forecasting methods used for intermittent demand forecasting.

This study proposes a hybrid forecasting framework that integrates the Auto-Regressive Integrated Moving Average (ARIMA) model with multiple variations of the Ata model, using a performance-based model selection strategy to enhance forecasting accuracy on the M3 and M4 competition datasets. For each time series, seven versions of the Ata model are generated by adjusting level and trend parameters, and the version with the lowest in-sample symmetric mean absolute percentage error (sMAPE) is selected. To improve robustness and prevent overfitting, the median-performing Ata model is also included. These selected models’ forecasts are then combined with ARIMA outputs through optimized weighting schemes tailored to the characteristics of each series. Given the varying frequencies (e.g., yearly, quarterly, monthly, weekly, daily, and hourly) and diverse lengths of time series, a grid search algorithm is employed to determine the best hybrid combination for each frequency group. The model is applied in a series-specific manner, allowing it to adapt to different seasonal, trend, and irregular patterns. Extensive empirical results demonstrate that the hybrid model outperforms its individual components and traditional benchmarks across all frequency categories. It ranked first in the M3 competition and achieved second place in the M4 competition based on the official error metric, the sMAPE and Overall Weighted Average (OWA), respectively. The results highlight the framework’s adaptability and scalability for complex, heterogeneous time series environments.

The field of competitive strategy has attracted growing academic interest in recent years; however, the intellectual framework and thematic evolution of this research area remain fragmented. This study aims to systematically map the evolution of competitive strategy research using an unsupervised machine learning framework. Drawing on a dataset of approximately 3900 journal articles indexed in the Scopus database between 2015 and 2025, the study employs probabilistic topic modeling, specifically Latent Dirichlet Allocation (LDA), together with keyword co-occurrence network analysis, thematic mapping, and community detection techniques to identify the latent thematic structure of the field. The findings reveal a modular and interconnected conceptual landscape in which capability-based strategic perspectives, particularly dynamic capabilities and the resource-based view, continue to occupy central positions in the literature. At the same time, themes related to digital transformation, artificial intelligence, supply chain resilience, environmental, social, and governance (ESG)-oriented management, and sustainability-focused strategic capabilities demonstrate substantial growth and emerging prominence. Temporal analyses further indicate a gradual reconfiguration toward digitally integrated, sustainability-oriented, and capability-driven strategic frameworks. By integrating topic modeling with network-based bibliometric analysis, the study provides a comprehensive and data-driven mapping of the field’s intellectual evolution. The study contributes to competitive strategy research by synthesizing the latent thematic structure of the field and by showing how complementary computational and bibliometric techniques can support large-scale literature mapping in a rapidly evolving research domain.

Purpose- This study explores the utilization of machine learning techniques in financial marketing to create a more efficient financial product, with understanding customer behavior and further drive the marketing efficiency as the primary focus. The research aims to highlight the role of advanced analytics in enhancing campaign effectiveness and targeting precision, thereby empowering businesses to make data-driven decisions in a competitive landscape. Methodology- Various machine learning models were used in the study, including Decision Trees, Random Forests, Gradient Boosting Machines, Naive Bayes, Support Vector Machines, LightGBM, and Long Short-Term Memory. Feature engineering, particularly the inclusion of interaction terms and ratio-based features, was a critical component of the methodology, enabling the capture of complex patterns in customer behavior data. Model performance was rigorously evaluated using metrics such as precision, recall, and F1-score to provide a comprehensive understanding of predictive capabilities. Findings- The machine learning models are confirmed to be very efficient in the investigation and identification of actionable than secret knowledge about consumer preferences and behavior. According to the research, these models were recently employed to develop accurate and tailored marketing approaches that greatly enhance campaign impact and targeting success. Things like precision, recall, and F1-score highlight the strengths or limitations of individual models and helped to guide the selection appropriately to specific marketing goals. Conclusion- This study highlights the emprical importance of machine learning methods into financial marketing processes. By using advanced analytics, businesses can refine their marketing strategies, improve campaign outcomes, and remain competitive in a rapidly evolving marketplace. The results highlight the revolutionary ability of machine learning to facilitate accurate data driven marketing choices relative to customer desires. Keywords: Financial marketing, machine learning, deep learning, data analytics, customer behavior prediction, campaign optimization

Sustainable development has become a strategic priority for countries seeking long-term economic prosperity while balancing social, environmental, and institutional objectives. According to the April 2026 International Monetary Fund World Economic Outlook projections, Türkiye is expected to become the largest economy among the selected major Muslim economies in terms of nominal gross domestic product. Motivated by this development, this study benchmarks Türkiye’s sustainable development performance against nine comparable economies and examines the relationships between selected Sustainable Development Goal (SDG) indicators and economic performance. The analytical framework integrates benchmarking, correlation, SDG network analysis, regression modeling, and explainable machine learning using SDG and macroeconomic data. The findings reveal both synergies and trade-offs among the selected SDGs. Strong positive relationships emerge between SDG12 and SDG13 and between SDG4 and SDG16, confirming the interconnected structure of sustainable development. Türkiye ranks second in overall SDG performance and performs particularly strongly in clean energy, innovation and infrastructure, and international partnerships. Regression results identify education, clean energy, innovation, and climate action as statistically associated with economic performance. Under a strict year-based chronological holdout and rolling-origin validation framework, the tuned Random Forest model achieved the strongest out-of-sample predictive performance. SHapley Additive exPlanations analysis indicates that one-year lagged economic performance is the dominant predictor, while current macroeconomic conditions and selected SDG indicators provide additional predictive information. The findings demonstrate substantial temporal persistence in economic performance and show that sustainability indicators complement prior economic information in forecasting. The study contributes to sustainability research by integrating comparative assessment, systems-oriented analysis, econometric modeling, and explainable machine learning while clearly distinguishing statistical association from predictive contribution.

Effective renewable energy policy requires benchmarks that reflect countries’ structural energy characteristics rather than generic performance rankings. This study develops an explainable machine-learning framework that identifies structurally comparable peers and derives peer-relative priorities, using annual World Bank data for 37 Organisation for Economic Co-operation and Development (OECD) countries over 2010–2021. Thirteen energy, economic and demographic indicators are standardized and analysed with principal component analysis, Ward hierarchical clustering and cosine similarity, supported by bootstrap, density-based, temporal, scaling and leave-one-country-out robustness checks. Random Forest with Gini, permutation and SHAP importance is used descriptively to characterize the clusters, and directionally filtered standardized gaps between Türkiye and its closest peer form a Strategic Gap Priority Index (SGPI). Three weakly separated regimes emerge: renewable-intensive economies, larger urbanized fossil-based economies, and less urbanized lower-income economies. Under historical averages, Türkiye and the United Kingdom are mutually closest peers, and coal-based electricity generation and net energy import dependence are the two leading SGPI priorities under all weighting schemes tested. These results are conditional on the profile definition: adding annual trends makes Hungary the closest peer, and excluding hydropower makes non-hydro renewable generation the second priority.