Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently,...

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand...

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand...

Accurate wind power forecasting is crucial for the effective scheduling and scientific management of wind energy, enhancing the safety and reliability of power grids. To handle the inherent intermittency of wind energy, forecasting methodologies have evolved from traditional statistical models...

This study introduces a novel machine learning-based methodology for detecting and forecasting the strength of weak signals in the labor market, using Greece as a case study and utilizing Eurostat time series data (2000-2023). Weak signals, conceptualized as subtle anomalies within otherwise...

Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated...

This study proposes a novel attention-based deep neural network (AttDNN) model specifically designed for predicting mergers and acquisitions (M&A). The model extends existing deep learning frameworks by incorporating M&A-specific features, regularization layers, and an attention mechanism that...

Projecting the effects of proposed policy reforms is challenging because no outcome data exist for regulations that governments have not yet implemented. We propose an ex ante deep learning framework that can project effects of proposed reforms by mapping outcomes observed under past regulations...

Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and...

Accurately predicting stock market movements remains a critical challenge in finance, driven by the increasing role of algorithmic trading and the centrality of financial markets in economic sustainability. This study examines the incorporation of artificial intelligence (AI) and machine...

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of graph...

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in...

Everyone needs access to public toilets, yet despite their importance in ensuring timely use, the shortage and limited availability of public toilets remain a global challenge. Conventional assessments of public toilet services often overlook actual usage patterns and focus solely on physical...

Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear...

Optimizing solid waste management (SWM) is essential for ensuring a sustainable and healthy environment in a city. This study considers a two-echelon solid waste logistics system (2E-SWLS) in a metropolitan city with a fleet of capacitated heterogeneous vehicles. The problem consists of waste...

In the automotive sector, vehicle data gathered through On-board Diagnostics (OBD) systems offers continuous insights into vehicle health status and performance. Leveraging this data for predictive maintenance can significantly reduce unplanned failures, enhance safety, and extend vehicle...

Imbalanced data is often encountered in scenarios where rare but critical events occur much less frequently than others, and it is particularly prominent in fields such as disease diagnosis, fraud detection, and risk management. The main problem with imbalanced data is that predictive models...

Insurance fraud detection is a significant problem in the insurance industry, producing immeasurable losses. Conventional insurance fraud detection models depend heavily on experts' knowledge, and accurately estimating fraud when the data and the claim data are enormous is a complex and...

Massive Open Online Courses (MOOCs) and the broad adoption of distance learning over the past few years have caused a remarkable shift in the educational landscape. However, the vast majority of available MOOCs often challenge learners in selecting courses that align with their academic goals,...