Search Results (2,397)

Accurate regional demand forecasting supports reliable operation of urban electric vehicle (EV) charging networks. However, public charging demand exhibits spatial heterogeneity, multi-scale periodicity, and uncertainty. This paper proposes Urban-CSTPNet, a multi-relational spatio-temporal probabilistic forecasting framework. Five semantically explicit graphs represent geographical adjacency, spatial distance, historical demand correlation, pricing-pattern similarity, and static regional attributes. Sample-level time-conditioned graph gating fuses these relations using historical demand states and calendar context. Independent recent, daily, and weekly branches capture short-term variation, daily repetition, and weekly regularity, and are combined through temporal gating. The model produces multiple conditional quantiles and applies horizon-specific conformalized quantile regression using an independent calibration set. Experiments on the Shenzhen UrbanEV dataset at 1, 3, 6, 12, and 24 h horizons achieve a mean absolute error (MAE) of 60.44, root mean squared error (RMSE) of 192.44, and Pinball Loss of 16.96. These values are 11.51%, 6.04%, and 9.50% lower than those of ST-MGF-Q. For calibrated 90% intervals, the prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and Interval Score are 0.9024, 0.0175, and 349.60. Results confirm improved forecasting accuracy, probabilistic quality, and empirical interval reliability. Full article

Exchange-listed medical data products create information and pricing asymmetry because sellers, buyers, and governance reviewers do not observe the same product boundaries, scenario permissions, processing depth, or compliance costs. This study proposes SM-DPF, an auditable pricing reference framework that makes these asymmetric conditions explicit through a reproducible three-layer chain: a public investment anchor, a locked structural scoring model, and a proposed future market learning governance interface that is not implemented or evaluated in the present study. Using 23 de-identified transaction-descriptive records from a single exchange context across insurance claims, model pretraining, and pharmaceutical R&D, the pricing reference outputs y_0,i show in-sample diagnostic consistency with de-identified transaction prices y_i, with an overall mean absolute percentage error of 4.82% and median absolute percentage error of 4.55%. The same 23 records informed the initial scenario-response calibration and the subsequent diagnostics; the reported errors and correlations are, therefore, in-sample diagnostics only. SM-DPF is a governance-oriented reference tool for transparent listing decisions and audit replay with no transaction price prediction claim. Full article

Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing remains sparse. This analysis examines the impact of climate risks on corporate bond credit spreads within the real estate sector by constructing three thematic indicators: transition risk (CTRI), chronic physical risk (ChroCPRI), and acute physical risk (AcuCPRI). Initial feature selection via machine learning suggests all three risk categories as predictive covariates for bond pricing. Subsequent regression estimations indicate that climate transition risk and acute physical risk expand credit spreads, whereas chronic physical risk compresses them—with these statistical patterns being more pronounced among state-owned enterprises (SOEs). Mechanism analyses yield threefold insights: first, transition risk elevates spreads by tightening financing constraints and restricting corporate asset growth, a channel concentrated in short-term tranches and low-liquidity firms; second, the counterintuitive spread-compressing effect of chronic risk is localized among firms with lower credit ratings and lower profitability, consistent with institutional climate support frameworks and strategic green adaptations; third, acute physical risk widens spreads by compressing operational cash flows and exacerbating financing friction, particularly for smaller enterprises. These channels align with the structural attributes of SOEs, which are characterized by larger asset scales, superior capital liquidity, and a higher propensity to secure state guarantees. Full article

Social and economic satisfaction gaps are theoretical differences between a resident’s socially or monetarily most preferred affordable home attributes and the actual attributes of their current home. In this study, the satisfaction gaps of two samples of respondents are predicted with their social utility data for preferred attributes in 1987 and 2020, in conjunction with the asking or sale prices and comparable attributes of single detached or similar homes merged with small-area census data for periods up to those dates. Average social and economic gaps of less than 20% affirm respondents’ overall satisfaction with their current homes, most of whom were recent movers. However, wider social satisfaction gaps among the minority of residents may reflect dissatisfaction with new suburban locations in one sample and older inner city housing in the other. Four practical and theoretical contributions of the utility modelling of residential satisfaction gaps in objective and subjective home attributes, as opposed to direct surveying of residential satisfaction, are concluded. Full article

In South Korea, apartments dominate the residential housing market, accounting for 67.5% of total housing transactions in the fourth quarter of 2018. With this figure continuing to rise, apartments are the most significant asset for many families. Consequently, precise and timely valuations are crucial for stakeholders, including homeowners, buyers, and mortgage lenders. Traditionally, these stakeholders have relied on the qualitative judgments of certified real estate agents. Because of market opacity and low liquidity, agents often use a comparative approach, referencing the most recent transaction prices of nearby comparable apartments. However, this method is subjective, potentially biased, time-consuming, and costly. Our study seeks to offer a more objective and quantitative method for determining fair apartment prices in Korea, helping market participants make informed decisions. The prediction target is the monthly representative price of an apartment complex (the within-complex average of transaction prices), from which a complex-level price index is subsequently constructed; we distinguish this target from individual transaction prices throughout. By employing clustering methods to identify similar apartments and imputation techniques for missing values, our model demonstrates promising results, with a mean absolute percentage error as low as 5.38% in the worst-case (consecutive-mask) setting and 4.76% in the typical (random-mask) setting. Because the training (2006–2015) and test (2016–2022) periods are temporally disjoint, these figures reflect out-of-sample performance rather than in-sample fit. We further validate the resulting series against external references: it attains a 5.03% MAPE against actual transactions nationwide—outperforming the appraiser-based Kookmin Bank index (7.29%)—and, once aggregated, closely tracks the official KREB transaction-based index while becoming available earlier; complex-level Granger tests confirm that our series temporally leads the appraiser-based series about 1.5 times as often as the reverse. We also outline the missing-data assumptions under which the approach is valid. Full article

Accurate short-term load forecasting for EV public charging stations is essential for grid and station operations. However, predictions are challenging because charging loads are non-stationary, spatially heterogeneous, and closely coupled with external factors such as weather and pricing. In this study, we forecast hourly regional charging energy using the open UrbanEV benchmark dataset, which includes hourly charging records from 1362 public charging stations across 275 traffic-analysis zones in Shenzhen from September 2022 to February 2023. We propose ST-Attention, a lightweight and modular forecasting model. It integrates temporal self-attention, spatial self-attention, an adjacency-matrix bias, and a residual prediction head. We compare ST-Attention with five baselines using a leakage-free rolling time-series evaluation protocol. For the 3 h horizon, ST-Attention achieves an MAE of 62.44 kWh, an RMSE of 291.8 kWh, and an MAPE of 5.91%, reducing the MAE by approximately 41% compared with the last-observation baseline. The model also maintains superior MAE performance at the 6 h and 9 h horizons. A modular ablation study shows that temporal attention and the residual head are the most stable sources of improvement, whereas dense spatial attention does not automatically provide benefits at hourly granularity with limited samples; removing it further reduces the 3 h MAE to 59.58 kWh. We present this as a cautionary finding: local temporal inertia dominates dense spatial coupling in hourly forecasting, and spatial model complexity must align with data granularity. Full article

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues. Full article

Carbon market prices are jointly shaped by policy interventions, energy market fluctuations, and macroeconomic dynamics, and thus exhibit pronounced nonlinearity, non-stationarity, localized abrupt changes, and time-varying uncertainty. From an information-theoretic perspective, carbon price forecasting can be viewed as the extraction and fusion of effective information from a complex market system driven by heterogeneous endogenous and exogenous signals. To address the challenges of accurately characterizing local high-frequency fluctuations in carbon price series, effectively modeling the interactions between endogenous and exogenous variables, and mitigating the structural noise introduced by conventional serial forecasting frameworks, this study proposes ConvTimeXer, a hybrid model combining bidirectional temporal convolution and TimeXer for carbon market price forecasting. Specifically, the model first employs front-end bidirectional temporal convolutions to extract local multi-scale fluctuation features from the endogenous carbon price series. It then leverages the global token and cross-attention mechanism in TimeXer to achieve dynamic decoupling and deep interaction between endogenous and exogenous variables. Finally, residual fusion of shallow and deep features is introduced to enhance the preservation of local details. Experimental results based on data from China’s carbon market over the past three years demonstrate that the proposed framework delivers high predictive accuracy and strong robustness, effectively balancing responsiveness to local abrupt changes with global trend modeling. This study not only provides an effective approach for carbon price forecasting in complex and uncertain market environments, but also offers valuable insights into non-stationary time-series forecasting driven by multi-source heterogeneous information. Full article

This study provides new evidence on the predictability of confidence indices in China’s stock returns. We demonstrate that, during the sample period from January 2005 to December 2022, the business confidence index (BCI) positively and significantly predicted subsequent stock market returns, outperforming mainstream economic predictors and other confidence indices. Further, for the pricing effectiveness of the stock market, the BCI and investor sentiment provide complementary sources of information. The predictive power of confidence indices for stock market returns declined significantly during the COVID-19 pandemic. Meanwhile, confidence indices predicted better during bear market periods compared to bull market periods. Finally, in practical investment applications, the BCI and alternative confidence index produce appreciable economic gains for investors. These empirical results also pass the robustness test. Full article

With the rapid growth of electric vehicles (EVs), high charging demand has increased uneven station utilization and pressure on distribution networks. Therefore, this paper proposes a collaborative method for capacity planning and charging scheduling of multiple charging stations (CSs) considering time-of-use (TOU) pricing and energy storage systems (ESSs). A total system cost model is established, including transformer and ESS costs. Secondly, a deep neural network-guided improved sparrow search algorithm (DNN-ISSA) is proposed to optimize the number of chargers and parking spaces by predicting the initial capacity center. Furthermore, a charging scheduling algorithm is proposed to optimize user charging time by introducing a TOU price response function to modify charging probabilities. A case study of 36 CSs in Jinan shows that the proposed method reduces average charging time by 15.7, 15.4, and 15.2 min for 1000, 5000, and 10,000 demand points, while lowering the total system cost from 73.92 to 70.36 million yuan. The convergence value of DNN-ISSA reduces by 15.05%, 21.67%, and 11.61% compared with the improved sparrow search algorithm (ISSA), particle swarm optimization algorithm (PSO), and sparrow-particle swarm optimization algorithm (SSA-PSO), respectively. The proposed method enhances energy utilization, mitigates peak loads, and supports low-carbon EV charging operation. Full article

Electricity price uncertainty directly affects the economy and reliability of scheduling, especially when flexible loads are scheduled only according to point forecasts. To improve the coupling between price forecasting uncertainty and load scheduling, this paper proposes a two-stage affine adjustable robust optimization method for flexible loads based on the confidence level of electricity price prediction via an improved hybrid convolutional neural network temporal convolutional network (CNN-TCN) model. An attention-enhanced CNN-TCN model is used to obtain day-ahead electricity price forecasts, and conformalized quantile regression (CQR) is introduced to construct calibrated asymmetric prediction intervals under different confidence levels. The interval bounds are then converted into a budgeted price uncertainty set and embedded in a two-stage affine adjustable robust optimization model for industrial, commercial, and residential loads. The model considers power limits, ramping constraints, total energy requirements, baseline deviation limits, and smoothing penalties, enabling load transfer from high-price periods to low-price periods while preserving operational feasibility. Case studies based on Spanish electricity market data show that the proposed method reduces operating costs under forecast, worst-case, and abnormal disturbance scenarios compared with the original load plan. The results also show that the 90% confidence level provides a suitable balance among cost reduction, risk coverage, and scheduling conservatism in the studied case. Full article

Accurate stock price forecasting remains a challenging problem due to the noisy, nonlinear, and non-stationary characteristics of financial time series. Although recent advances in deep learning have improved predictive capabilities, most prior studies evaluate forecasting models primarily using statistical error metrics, with limited consideration of their practical value in trading and investment decision-making. This creates a gap between predictive performance and real-world economic utility. This study proposes a decision-oriented evaluation framework for multi-step stock price forecasting that jointly assesses predictive accuracy and trading profitability within a unified experimental setting. Using data from 91 ASX 100 stocks after data cleaning, with a testing period spanning 2019–2020, several deep learning architectures, including Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), Seq2Seq, and attention-based sequence models, are systematically compared under identical training and trading conditions. The results show that the Seq2Seq model achieved the best overall performance, obtaining the lowest average MAPE of 0.0293 and the highest ROI of 23.2%, while the attention-based model achieved a similar MAPE of 0.0294 but a lower ROI of 12.4%. Although differences in forecasting accuracy were relatively small, the Seq2Seq model achieved the highest observed trading profitability and generated a higher observed return than a passive market benchmark under the proposed evaluation framework. These findings suggest that evaluation based solely on prediction accuracy may not fully capture the practical value of forecasting models. Full article

Gold’s price reflects currency, opportunity-cost, and safe-haven channels whose strength shifts across regimes, motivating an empirical, data-driven forecasting approach. This study develops a monthly gold price forecasting system for ASM sales-timing decisions in Peru (January 2020–June 2026) using macro-financial predictors including a geopolitical risk index and three U.S. monetary indicators, none of which were Granger-causal and were therefore excluded from the production set. After confirming non-stationarity and Johansen cointegration (four vectors), thirty-two model-feature-set combinations, including Elastic Net, Bayesian Ridge, and a PCA factor, were compared under strict temporal validation with bounded hyperparameter search. The selected model, Ridge regression on the CONTROL feature set, achieved a cross-validation MAPE of 2.29% and test MAPE of 3.62% (official)/3.15% (extended sensitivity window). It was benchmarked against random walk, historical mean, and exponential smoothing and evaluated via the Diebold–Mariano, Clark–West, encompassing, and Model Confidence Set tests (low-power caveats given the small sample). A dual-horizon Monte Carlo simulation, robust to heavy-tailed shocks, projected USD 4482/oz (December 2026) and USD 5106/oz (December 2027). A sales-timing backtest showed a statistically significant result (−0.67%) versus a passive strategy, indicating calibrated price information alone does not yet yield a reliable trading edge, supporting the model’s role as decision support rather than an autonomous trading signal. Full article

This study proposes a hybrid deep learning model named LSTM-CBAM-Time2Vec-KAN based on symmetry awareness and optimized multi-module collaborative features, aiming to improve the accuracy and stability of stock price prediction. To address common shortcomings in traditional forecasting models such as insufficient feature extraction, difficulties in parameter optimization, and inadequate utilization of temporal characteristics, the research innovatively exploits the symmetry inherent in financial time series, particularly their temporal periodicity and cross-dimensional feature consistency, to construct an intelligent prediction framework that integrates multiple modules. First, wavelet transform is applied to perform multi-scale decomposition and signal reconstruction on the raw stock price sequence, effectively extracting high signal-to-noise ratio features. Second, the Northern Goshawk Optimization (NGO) algorithm is employed to jointly optimize key hyperparameters of the model, including the LSTM hidden layer dimension and CBAM compression ratio, thereby resolving the challenge of parameter coupling across modules. Third, the CBAM attention mechanism enhances the importance of temporal features extracted by LSTM through a dual mechanism of channel and spatial attention, enabling the model to focus on critical price movement points. Meanwhile, Time2Vec encoding transforms temporal information into embedding representations with periodic properties, effectively capturing cyclical patterns at daily, weekly, and monthly trading intervals. Finally, the Kolmogorov–Arnold network (KAN) fuses multimodal features and produces precise predictive outputs. Experimental results show that the proposed model significantly outperforms all baseline models in four evaluation metrics, namely mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²), which verifies its superior prediction accuracy and robustness. Furthermore, analyses of stock price forecasting under different time spans and simulated trading performance under various trading strategies further demonstrate that this study provides a feasible and effective technical solution for financial time-series forecasting, with important theoretical research value and practical application value. Full article

Electric vehicle (EV) charging has a large, spatially clustered, schedulable load whose vehicle-to-grid flexibility can be sold back to the power system. That flexibility has grid value only when the committed quantity can be reliably delivered under uncertainty. Open forecasting benchmarks operators rely on report-only point predictions. The dispatch models that turn forecasts into firm commitments assume a constant round-trip efficiency, so the committed flexibility is systematically over-scheduled. This study contributes two complementary modules, validated separately on public data. The first is a calibrated probabilistic charging forecaster that provides, to our knowledge, the first prediction intervals with reported empirical coverage on the UrbanEV benchmark. It is a gradient-boosted quantile-regression model that combines each zone’s own-history lags with adjacency-weighted neighbor-mean features and exogenous price and calendar inputs. It is calibrated by conformalized quantile regression and scored over thirty zones across a 120-day hourly window. The second is a deliverable-flexibility envelope whose returnable-energy bounds are set by measured, state-of-charge- and rate-dependent vehicle-to-grid (V2G) discharge efficiency rather than a constant round-trip number. These bounds are fit to the measured discharge traces of three V2G-capable vehicles in the Esser bidirectional-charging dataset. Chosen as a lightweight, reproducible baseline, the forecaster keeps its prediction intervals within a five-percentage-point coverage tolerance at both the 80% and 90% nominal levels. Measured coverage is 0.823 and 0.911. It also improves on the continuous ranked probability score of its conformalized-point counterpart at matched point accuracy. This calibration holds across the hyperparameter neighborhood and under data deficiency. On the delivery side, a leave-one-vehicle oracle shows the efficiency-aware envelope short-delivers less than the constant-average-efficiency aggregator on held-out vehicles. Its residual shortfall is 1.21% against the aggregator’s 2.03% at the conservative operating point. The margin widens as commitments grow more aggressive and discharges reach the lowest states of charge. Each of these two measured properties, calibrated demand-side uncertainty and state-dependent discharge physics, imposes a material, separately validated constraint on how much contracted EV flexibility can be delivered, a constraint the point-forecasting frontier leaves unaddressed. Full article