Omer Faruk Beyca

Operational apparel forecasting requires comparisons that combine strong classical baselines, modern learning architectures, sensitivity analysis, and evaluation across multiple forecast origins. This study benchmarks nine methods for one-step-ahead daily forecasting using 1403 observations of a retailer-defined potential-sales measure from 11 apparel categories in Türkiye. The methods are seasonal naïve (SNaive), exponential smoothing (ETS), Light Gradient Boosting Machine (LightGBM), feedforward neural network (FFNN), standard Kolmogorov–Arnold Network (KAN), long short-term memory (LSTM), Transformer, TimeKAN, and iTransformer, all evaluated under a lag-only information setting. Evaluation combines a 183-day test window, a five-method joint-versus-per-category ablation, fixed-configuration look-back sensitivity, and seven rolling-origin windows. ETS achieved the lowest macro weighted absolute percentage error (WAPE; 12.02%) and mean absolute scaled error (MASE; 0.690) on the test window, while iTransformer and TimeKAN were the strongest learning-based alternatives. Across rolling origins, iTransformer achieved the lowest mean macro WAPE, whereas ETS led five of seven windows and achieved the lowest mean pooled WAPE and macro MASE. Standard KAN showed intermediate performance, while TimeKAN and iTransformer improved with longer look-back windows under fixed configurations. Per-category configurations yielded lower WAPE and MASE values than matched joint configurations in the five-method ablation. Overall, rankings depended on forecast origin, error aggregation, historical context, and information-sharing design.

Intermittent sales patterns, prevalent in fast-fashion retail, pose a critical challenge for conventional forecasting methods. This study empirically compares one-stage and two-stage machine learning (ML) frameworks with classical benchmarks (Croston, SBA). The two-stage approach uses a Random Forest classifier for demand occurrence, followed by regression models (RF, GBM, XGBoost, LightGBM) for magnitude. Models are evaluated using weekly sales data from an Iraqi fashion retailer, incorporating rich exogenous features like product attributes, pricing, weather, and special events across 64 unique attribute-defined product group time series. Performance is assessed via a fixed 13-week holdout and rolling-origin cross-validation, with LSTM and Temporal Fusion Transformer (TFT) serving as deep learning benchmarks. Empirical findings show that machine learning configurations achieve superior WRMSSE accuracy, with two-stage models often outperforming one-stage counterparts, and both significantly surpassing classical and deep learning baselines. The Two-Stage XGBoost yielded the lowest WRMSSE, establishing the feature-engineered two-stage framework as the strongest overall for this intermittent retail setting. Furthermore, a detailed SHAP analysis elucidated the distinct feature contributions to demand occurrence versus demand magnitude, providing actionable insights for inventory management. This rigorous benchmarking analysis offers practical implications for inventory planning and demand management in volatile markets, highlighting the effectiveness of explicit demand occurrence modeling.

Wind speed forecasting is vital for energy planning and grid integration in regions with high wind potential and growing renewable energy deployment. Conventional recurrent models such as Long Short-Term Memory (LSTM) networks capture temporal dependencies effectively but struggle to generalize across heterogeneous wind regimes. To overcome these limitations, this study proposes a Residual Mixture-of-Experts (ResMoE) architecture, building on the Mixture-of-Experts (MoE) framework, where multiple LSTM experts are combined through a gating mechanism to model different components of the data. The ResMoE introduces a global expert that captures large-scale temporal dynamics within each province, along with residual experts that model localized deviations as additive corrections. This decomposition enables the model to separate shared patterns from regime-specific variations, improving both learning stability and specialization. Experiments were conducted using long-term meteorological datasets (~210,000 hourly records per site) from five representative provinces in Turkey (Balıkesir, Çanakkale, İzmir, Kırklareli, and Tekirdağ) and each province is characterized by distinct wind regimes. Input features included 9 meteorological variables, with a 24-hour lag window used to model short-term dependencies. Models were trained using the AdamW optimizer with learning rate scheduling and early stopping, and evaluated using the Mean Absolute Percentage Error (MAPE) across multiple randomized runs, along with complementary error metrics and supported by statistical significance analysis. Results show that the proposed ResMoE consistently achieves the lowest forecasting errors across all provinces. Across the five study sites, ResMoE reduces MAPE by approximately 6.5–10.3% relative to the baseline LSTM and by about 1.8–6.0% relative to the standard Mixture-of-Experts (MoE). The residual formulation improves training stability by anchoring predictions to a global baseline and reduces expert collapse by limiting each expert to modeling residual errors. Additionally, the additive structure allows direct analysis of expert contributions, providing a more interpretable forecasting framework. Overall, ResMoE provides a structured and robust approach for heterogeneous time-series forecasting, with applicability beyond wind energy.

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models.

With the development of technologies and the growing need for accurate positioning inside buildings, the localization method based on Received Signal Strength Indicator (RSSI) fingerprinting is becoming increasingly popular. Its popularity is explained by the relative simplicity of implementation, low cost and the ability to use existing wireless infrastructure. This review article covers all the key aspects of building such systems: from the wireless communication technology and the creation of a radiomap to data preprocessing methods and model training using machine learning (ML) and deep learning (DL) algorithms. Specific recommendations are provided for each stage that can be useful for both researchers and practicing engineers. Particular attention is paid to such important issues as RSSI signal instability, the impact of multipath propagation, differences between devices and system scalability issues. In conclusion, the review highlights the most promising areas for further research. For smart cities, the approaches and recommendations presented in the review contribute to the development of urban services by combining indoor positioning systems with IoT platforms for automation, transport and energy management.

Technological progress and digitalization have ushered in significant transformations in business strategies. At present, research is scarcely focused on the influence of the adoption of digital technologies (DTs) on establishing comprehensive relationships within the context of a circular economy (CE), and the supply chain (SC) framework to contribute to the Resource-Based View (RBV) theory. This study utilizes survey data collected from 235 manufacturing practitioners employed by Turkish manufacturing enterprises to explore a model elucidating the relationship between DTs adoption and sustainability performance (SP) through supply chain traceability (SCT), supply chain resilience (SCR), and circular economy practices (CEPs), based on 10R strategies. Through this linkage, this research accentuates that the exclusive integration of CEPs with digital technology solutions is insufficient for industrial enterprises to attain their long-term sustainability goals. It underscores the necessity of ensuring SCT and/or SCR in this context.