Search Results (140)

Stock-ranking prediction is an important task in quantitative finance because it directly influences portfolio construction and alpha generation. Recent Graph Neural Network (GNN) models provide a promising way to describe inter-stock dependencies, but many existing methods still have difficulty balancing rapidly changing market interactions with relatively stable structural relationships. They are also easily affected by financial micro-structure noise. To address these issues, this paper proposes USTGCN, a Unified Spatio-Temporal Graph Convolutional Network for stock-ranking prediction. USTGCN adopts a dual-stream temporal encoder based on ALSTM and GRU to capture short-term dynamic patterns and longer-horizon structural information, respectively. We further introduce a rolling-window correlation smoothing strategy to build a more stable dynamic graph, and then integrate the dynamic and structural graph views through a shared fusion layer. Skip connections are used to preserve original temporal information during spatial aggregation. Experiments on the CSI100 and CSI300 benchmark datasets show that USTGCN achieves IC values of 0.141 and 0.154, respectively, and exhibits improved drawdown control during stressed market periods, indicating its practical value for quantitative trading. Full article

Portfolio selection is a fundamental task in quantitative finance that aims to allocate capital across assets to balance risk and return. While deep learning has shown great promise in this field, extracting reliable feature representations from non-stationary and noisy financial data remains a significant challenge. The existing models often fail to simultaneously capture the temporal dynamics of price series and complex inter-asset correlations, which limits their trading performance. To address these issues, we propose Denoising-Sequence-Correlation Reinforcement Learning (DSCRL), a novel portfolio selection framework based on deep reinforcement learning. DSCRL employs a dual-stream feature extraction network, where one stream aims to learn temporal market dynamics and the other aims to capture asset correlations, enabling more informative representations. A denoising module is further integrated to mitigate the impact of noise, ensuring stability and robustness in the learning process. Furthermore, a deterministic policy gradient (DPG)-based decision network is designed to directly optimize continuous portfolio weights and normalize them to satisfy budget constraints while preserving the importance. Extensive experiments conducted on multiple benchmark datasets demonstrate that DSCRL consistently outperforms both traditional financial heuristics and advanced deep reinforcement approaches. The results highlight its superior ability to achieve higher cumulative returns with lower volatility. Overall, DSCRL provides an effective and robust solution that strikes a better trade-off between pursuing profits and managing risks in dynamic financial markets. Full article

As deep learning models become increasingly integrated into critical decision-making systems, the need for explainable Artificial Intelligence (xAI) has grown paramount to ensure transparency, accountability, and trust. Post hoc explainability methods, which analyse trained models to interpret their predictions without modifying the underlying architecture, have become increasingly important, especially in fields such as healthcare and finance. Modern xAI techniques often produce feature importance rankings that fail to capture the true causal influence of features, particularly in transformer-based models. Recent quantitative metrics, such as Symmetric Relevance Gain (SRG), which measures the area between the feature corruption performance curves of the Most Important Feature (MIF) and the Least Important Feature (LIF), provide a more rigorous basis for evaluating explanation fidelity. In this study, we first show that existing xAI methods exhibit consistently poor performance under the SRG criterion when explaining transformer-based text classifiers. To address these limitations, we introduceEvoDropX, a novel framework that formulates explanation as an optimisation problem. EvoDropX leverages Grammatical Evolution (GE) to evolve sequences of feature corruption with the explicit objective of maximising SRG, thereby identifying features that most strongly influence model predictions. EvoDropX provides interventional, input–output (behavioural) explanations and does not attempt to infer or interpret internal model mechanisms. Through comprehensive experiments across multiple datasets (IMDb movie reviews (IMDB), Stanford Sentiment Treebank (SST-2), Amazon Polarity (AP)), multiple transformer models (Bidirectional Encoder Representations from Transformers (BERT), RoBERTa, DistilBERT), and multiple metrics (SRG, MIF, LIF, Counterfactual Conciseness (CFC)), we demonstrate that EvoDropX significantly outperforms all state-of-the-art (SOTA) xAI baselines including Attention-Aware Layer- Wise Relevance Propagation for Transformers (AttnLRP), SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME), when evaluated using intervention-based faithfulness criteria. Notably, EvoDropX achieves $74.77\%$ improvement in SRG than the best-performing baseline on the IMDB dataset with the BERT model, with consistent improvements observed across all dataset-model pairs. Finally, qualitative and linguistic analyses reveal that EvoDropX captures both sentiment-bearing terms and their structural relationships within sentences, yielding explanations that are both faithful and interpretable. Full article

Decarbonizing the transportation sector is critical for China to achieve its ambitious “dual-carbon” goals of peaking carbon dioxide emissions before 2030 and attaining carbon neutrality by 2060. Guided by the overarching philosophy of “ecological civilization,” this paper systematically reviews the historical evolution, technological pathways, and global implications of China’s transport transition. We analyze the institutional trajectory as governance shifts from early administrative mandates focused on energy conservation to a sophisticated, market-oriented framework incorporating carbon trading and green finance. The study identifies a synergistic technical pathway centered on the widespread adoption of new-energy vehicles (NEVs), the deep integration of renewable energy, and the deployment of intelligent transportation systems (ITSs) to enhance operational efficiency. Beyond domestic progress, the review highlights significant global spillover effects: China’s massive deployment scale and manufacturing capabilities have accelerated technological learning, driving down costs for batteries and clean technologies, thereby lowering adoption barriers worldwide. Furthermore, by reshaping green industrial value chains and actively engaging in global climate governance, China plays a pivotal role in fostering international technology diffusion. Ultimately, this review offers valuable insights into the complexity of systemic decarbonization, demonstrating how the coordination of policy guidance, technological innovation, and market mechanisms can advance sustainable development and effective emission reductions on a global scale. Full article

Under the framework of the Paris Agreement, carbon trading has emerged as a pivotal market-based instrument for achieving carbon neutrality. Following years of pilot programs, China has taken a critical step toward establishing a unified national carbon market. Consequently, accurate carbon price forecasting is essential for constructing a stable and effective carbon pricing mechanism. However, the 2017 reform of the EU Emissions Trading System (EU ETS) significantly altered the carbon price formation mechanism, exacerbating price volatility and uncertainty. This shift further underscores the urgent need for research into high-precision carbon price forecasting.Existing deep learning models struggle to simultaneously capture short-term high-frequency fluctuations and long-term evolutionary trends within complex carbon market data, a limitation that compromises their prediction accuracy and stability. To address these challenges, this paper proposes a Transformer-based carbon price forecasting model that incorporates an sLSTM structure. By enhancing sequence memory and state update mechanisms, this model effectively improves the capability to model both short-term volatility characteristics and long-term evolutionary patterns of carbon prices. In the data preprocessing phase, Variational Mode Decomposition (VMD) is employed to perform multi-scale decomposition of carbon price sequences, effectively mitigating the issue of overlapping fluctuations across different time scales. Furthermore, the Whale Optimization Algorithm (WOA) is utilized to optimize the number of decomposition modes and the penalty factor, thereby resolving the parameter sensitivity issues inherent in modal decomposition. Experimental results on real-world carbon price datasets demonstrate that the model achieves an average coefficient of determination ($R^2$) of 0.9862 and a Mean Absolute Percentage Error (MAPE) of only 0.5607%. These findings indicate that the proposed method possesses significant advantages in characterizing the complex dynamic features of time series, thereby effectively enhancing prediction accuracy.The proposed model can serve as a supportive tool for carbon-market risk monitoring and policy evaluation by identifying abnormal fluctuations and mitigating market inefficiencies caused by information asymmetry. This enhances the stability and predictability of carbon price signals as incentives for emissions reduction, enabling firms to plan abatement pathways and low-carbon investments, and strengthening the sustainable role of carbon markets in achieving carbon neutrality. Full article

Generative Artificial Intelligence (AI) has transitioned from a research milestone to a general-purpose technology with wide-ranging implications for organizations, labor markets, and information systems. Thanks to improvements in deep learning, generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, transformer-based language models, and reinforcement learning from human feedback (RLHF), generative AI can now create high-quality text, images, audio, code, and other types of content. This review synthesizes the core technical foundations and best practices for training, evaluation, and governance, with an emphasis on scalability and human oversight. The paper examines applications across customer service, marketing, software development, healthcare, finance, law, logistics, and the creative industries, and assesses the labor implications of generative AI using a sociotechnical lens. This study also develops a disruption index that integrates task exposure, adoption rates, time savings, and skill complementarity. The paper concludes with actionable recommendations for policymakers, organizations, and workers, emphasizing the importance of reskilling, algorithmic transparency, and inclusive innovation. Taken together, these contributions situate generative AI within broader debates about automation, augmentation, and the future of work. Full article

Cryptocurrency trading presents significant challenges due to extreme market volatility, rapid regime transitions, and non-stationary dynamics that render traditional trading strategies ineffective. Existing reinforcement learning approaches for cryptocurrency trading typically employ simplistic profit-based reward functions that fail to adequately capture risk management considerations, market microstructure costs, temporal dependencies, and regime-specific optimal behaviors. This limitation often results in strategies that perform well during favorable market conditions but suffer catastrophic losses during downturns. This paper introduces five novel reward functions grounded in economic utility theory, market microstructure, behavioral finance, adaptive risk management, and regime-conditional optimization. We systematically evaluate these reward functions across three reinforcement learning algorithms (Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic) and four distinct market regimes (bull, bear, high volatility, and recovery), using Bitcoin hourly data from 2018–2022. Our comprehensive experimental evaluation demonstrates that the Adaptive Risk Control reward function achieves exceptional performance, with a Sharpe ratio of 2.47, cumulative return of 26.4%, and maximum drawdown of only 16.8% during the predominantly bearish 2022 test period. Critically, regime-specific analysis reveals substantial performance heterogeneity: Adaptive Risk Control excels during high volatility (Sharpe ratio 3.21), while Temporal Coherence and Asymmetric Market-Conditional rewards dominate in trending and bear markets, respectively. These findings establish that sophisticated, theory-grounded reward engineering—rather than algorithmic innovations alone—constitutes the primary lever for improving RL trading systems, enabling positive risk-adjusted returns even during severe market downturns. Full article

Amid the exponential growth of streaming data and rising demands for real-time decision-making, Online Convex Optimization (OCO) has emerged as a foundational framework for sequential data processing in dynamic environments. This survey presents a systematic review of recent evolutionary and adaptive OCO strategies, offering a detailed taxonomy that classifies algorithms according to their constraint-handling mechanisms and environmental feedback. The analysis first examines Constrained OCO, elucidating the trade-offs between computational efficiency and theoretical guarantees across projection-based methods, projection-free Frank–Wolfe variants, and general convex optimization approaches. It then explores the Unconstrained OCO landscape, emphasizing the shift from parameter-dependent methods to fully adaptive, parameter-free algorithms capable of handling unknown comparator norms and gradient scales. Furthermore, the study synthesizes state-of-the-art applications in power systems, network communication, and quantitative finance, bridging theoretical OCO models with robust engineering solutions. The paper concludes by outlining critical open challenges and future research directions, such as the integration of OCO with deep learning, non-convex optimization, and robustness against adversarial corruptions in data-intensive scenarios. Full article

Data classification tasks based on deep neural networks and machine learning are increasingly used in different fields, such as medicine, finance, and data circulation. However, in these applications, the accuracy of predictions must be guaranteed, and the privacy and security of prediction data and models must be guaranteed. In an unsafe cloud environment, cloud users are reluctant to use the classification prediction tasks provided by the cloud. To solve these problems, this paper researches the data oversampling method and proposes the G-MSMOTE method, which can solve the oversampling problem of multiple minority classes in the data set, generate more diverse data, and solve the data imbalance problem. By improving the traditional FV and using CRT technology to improve coding efficiency, the cloud receives the user’s encrypted ciphertext, and the neural network completes the data prediction task in the ciphertext, thereby providing confidentiality for user data and model parameters under the semi-honest adversarial model, assuming the security of the underlying fully homomorphic encryption scheme and accepting the leakage of model architecture and ciphertext sizes. The feasibility of our method was demonstrated through experimental comparative analysis. We created unbalanced cases based on the MNIST dataset and performed comparative analysis in plain and ciphertext. In the balanced dataset, the model’s prediction accuracy in ciphertext reached 93.44%. In the unbalanced case, after preprocessing with our improved G-MSMOTE algorithm, the model’s prediction accuracy in ciphertext increased by at least 10%. These results show that our scheme can efficiently, accurately, and securely (under the semi-honest model) complete the data classification prediction task. Full article

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 physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

Accurate forecasting in low-liquidity futures markets is essential for effective trading. This study introduces a hybrid decision-support framework that combines Mini-TAIEX (MTX) futures data with sentiment signals extracted from 13 financial news sources and PTT forum discussions. Sentiment features are generated using three domain-adapted large language models—FinGPT-internLM, FinGPT-llama, and FinMA—trained on more than 360,000 finance-related texts. These features are integrated with technical indicators in four deep learning models: LSTM, GRU, Informer, and PatchTST. Experiments from June 2024 to June 2025 show that sentiment-augmented models consistently outperform baselines. Backtesting further demonstrates that the sentiment-enhanced PatchTST achieves a 526% cumulative return with a Sharpe ratio of 0.407, highlighting the value of incorporating sentiment into AI-driven futures trading systems. Full article

Clustering plays a central role in uncovering latent structure within both temporal and visual data. It enables critical insights in various domains including healthcare, finance, surveillance, autonomous systems, and many more. With the growing volume and complexity of time-series and image-based datasets, there is an increasing demand for robust, flexible, and scalable clustering algorithms. Although these modalities differ—time-series being inherently sequential and vision data being spatial—they exhibit common challenges such as high dimensionality, noise, variability in alignment and scale, and the need for interpretable groupings. This survey presents a comprehensive review of recent advancements in clustering methods that are adaptable to both time-series and vision data. We explore a wide spectrum of approaches, including distance-based techniques (e.g., DTW, EMD), feature-based methods, model-based strategies (e.g., GMMs, HMMs), and deep learning frameworks such as autoencoders, self-supervised learning, and graph neural networks. We also survey hybrid and ensemble models, as well as semi-supervised and active clustering methods that leverage minimal supervision for improved performance. By highlighting both the shared principles and the modality-specific adaptations of clustering strategies, this work outlines current capabilities and open challenges, and suggests future directions toward unified, multimodal clustering systems. Full article

Anomaly detection in financial transactions is a challenging task, primarily due to severe class imbalance and the adaptive behavior of fraudulent activities. This paper presents a reinforcement learning framework for fraud detection (RLFD) to address this problem. We train a deep Q-network (DQN) agent with a long short-term memory (LSTM) encoder to process sequences of financial events and identify anomalies. On a proprietary, highly imbalanced dataset, 10-fold cross-validation highlights a distinct trade-off in performance. While a gradient boosted trees (GBT) baseline demonstrates superior global ranking capabilities (higher ROC and PR AUC), the RLFD agent successfully learns a high-recall policy directly from the reward signal, meeting operational needs for rare event detection. Importantly, a dynamic orthogonality analysis proves that the two models detect distinct subsets of fraudulent activity. The RLFD agent consistently identifies unique fraudulent transactions that the tree-based model misses, regardless of the decision threshold. Even at high-confidence operating points, the RLFD agent accounts for nearly 30% of the detected anomalies. These results suggest that while tree-based models offer high precision for static patterns, RL-based agents capture sequential anomalies that are otherwise missed, supporting for a hybrid, parallel deployment strategy. Full article

Sustainable finance increasingly relies on Environmental, Social, and Governance (ESG) data, yet forecasting ESG-based stock indices remains challenging in an emerging-market context. Using Thailand as a representative case due to limited historical information, this study constructs a realistic simulated SET ESG Index using free-float-adjusted market capitalization and semiannual rebalancing rules that reflect the methodology of the Stock Exchange of Thailand. Using this index as the forecasting target, this study compares traditional statistical time series models (ARIMA, SARIMA, SARIMAX) with seven deep learning architectures (RNN, GRU, LSTM, DF-RNN, DeepAR, DSSM, Deep Renewal) to evaluate performance in multi-step (36-day) prediction. Results reveal that deep learning models significantly outperform statistical approaches, with GRU delivering the highest accuracy and the most consistent robustness across reduced-data scenarios. These findings highlight the ability of advanced AI techniques to capture nonlinear ESG market dynamics better. This study provides a replicable modeling pipeline for ESG index forecasting in data-constrained contexts, with practical implications for sustainable investment decision-making, risk management, and market resilience in emerging economies. Full article

Time series and spatio-temporal forecasting are fundamental tasks for complex system modeling and intelligent decision-making, with broad applications in transportation, meteorology, finance, healthcare, and public safety. Compared with simple univariate time series, real-world spatio-temporal data exhibit rich temporal dynamics and intricate spatial interactions, leading to heterogeneity, non-stationarity, and evolving topologies. Addressing these challenges requires modeling frameworks that can simultaneously capture temporal evolution, spatial correlations, and cross-domain regularities. This survey provides a comprehensive synthesis of forecasting methods, spanning statistical algorithms, traditional machine learning approaches, neural architectures, and recent generative and causal paradigms. We review the methodological evolution from classical linear models to deep learning–based temporal modules and emphasize the role of attention-based Transformers as general-purpose sequence architectures. In parallel, we distinguish these architectural advances from pre-trained foundation models for time series and spatio-temporal data (e.g., large models trained across diverse domains), which leverage self-supervised objectives and exhibit strong zero-/few-shot transfer capabilities. We organize the review along both data-type and architectural dimensions—single long-term time series, Euclidean-structured spatio-temporal data, and graph-structured spatio-temporal data—while also examining advanced paradigms such as diffusion models, causal modeling, multimodal-driven frameworks, and pre-trained foundation models. Through this taxonomy, we highlight common strengths and limitations across approaches, including issues of scalability, robustness, real-time efficiency, and interpretability. Finally, we summarize open challenges and future directions, with a particular focus on the joint evolution of graph-based, causal, diffusion, and foundation-model paradigms for next-generation spatio-temporal forecasting. Full article