The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and na...The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie...Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.展开更多
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using...It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradi...Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.展开更多
As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
Deep learning methods are increasingly employed to forecast air quality from an everincreasing stream of data and algorithms.However,the efficacy of current approaches may be questionable when evaluated not solely in ...Deep learning methods are increasingly employed to forecast air quality from an everincreasing stream of data and algorithms.However,the efficacy of current approaches may be questionable when evaluated not solely in terms of greater forecasting fidelity,but also concerning the decision-making process in pollution early warning.Here,rather than amending classical machine learning algorithms,we argue that now is the time to push the frontiers of air pollutant forecasting beyond state-of-the-art approaches.This can be achieved through near real-time assimilation of multiscale observations for laying the foundation of training data,enhanced attribution methods for impending heavy pollution,diagnostics for forecasting uncertainty,and advanced climate-chemistry emulators for improving seasonal forecasting.To harness this potential,it is essential to address several key challenges in deep learning methods,particularly generalization ability in extreme events,physics-informed interpretable approaches,and the mitigation technology of cumulative errors in multi-process coupled systems.This interdisciplinary endeavor will remain a central pursuit in the quest to anticipate and manage environmental change.展开更多
Dear Editor,The food industry emphasizes improving demand forecasting to align production with consumer needs and reduce waste.This letter thus presents a study that integrates artificial intelligence(AI)and digital t...Dear Editor,The food industry emphasizes improving demand forecasting to align production with consumer needs and reduce waste.This letter thus presents a study that integrates artificial intelligence(AI)and digital twin(DT)technologies to enhance decision-making and efficiency in food production.A data-driven DT was implemented in an Italian company for Raspberry production planning,based on a daily demand forecasting tool powered by a dynamic extreme gradient boosting(XGBoost)algorithm.The model achieved a mean absolute percentage error(MAPE)of 16.37%with 1.69 average of absolute extra working hours(AEW)and a tracking signal(TS)range of[−1.9,+4.3].展开更多
The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and a...The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.展开更多
The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this...The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this study proposes an innovative hybrid approach that integrates wavelet neural network(WNN)with chaos analysis.By leveraging the Cross-Correlation(C−C)method,the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN.Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals,advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators.Moreover,this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.展开更多
Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additi...Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.展开更多
In this study,we examine the connectedness between the NASDAQ artificial intelligence index and sectoral cryptocurrency indices.Empirical analyses were conducted via the quantile‒quantile methodology and cross-multiqu...In this study,we examine the connectedness between the NASDAQ artificial intelligence index and sectoral cryptocurrency indices.Empirical analyses were conducted via the quantile‒quantile methodology and cross-multiquantilogram tests across 15 cryptocurrency sectors from June 1,2021,to May 28,2024.The results show that dynamic total spillovers primarily occur in extremely low and high quantiles,corresponding to the left and right tails of the return distributions.Net directional spillovers indicate the dominance of the AI sector over the cryptocurrency market,which intensifies during significant crashes or booms.The most substantial effect of AI is observed in the DeFi,NFT,and Smart Contracts sectors,highlighting the prominence of financial operation-based blockchain applications in their interaction with artificial intelligence.The cross-multiquantilogram results also suggest that developments in artificial intelligence dominate the cryptocurrency market and have high predictability in its price movements.On the basis of our findings,we recommend using the AI market as an early indicator for the cryptocurrency market and advise against combining these two asset groups in the same portfolio to maintain diversification benefits.展开更多
Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we prop...Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.展开更多
Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the au...Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.展开更多
Based on ground observation data of relative humidity,the prediction performance of STNF and MIFS in each competition area during February 13-26,2024 was tested and evaluated by using two intelligent forecasting metho...Based on ground observation data of relative humidity,the prediction performance of STNF and MIFS in each competition area during February 13-26,2024 was tested and evaluated by using two intelligent forecasting methods(STNF and MIFS).The results show that STNF had better performance in forecasting relative humidity in high-altitude areas,and was suitable for fine forecasting under complex terrain.MIFS improved the short-term forecast of some low-altitude stations,but the long-term reliability was insufficient.STNF method performed better than MIFS during 0-24 h.As the prediction time extended to 24-72 h,the errors of both methods showed a systematic increase trend.STNF had higher precision,lower root mean square error and smaller mean error in most regions under the background of most weather systems,showing its superiority as a forecasting method of relative humidity.However,the precision of MIFS was slightly higher than that of STNF in Liangcheng without system background,revealing that MIFS may also be an effective option in some specific conditions.展开更多
Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting it...Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.展开更多
基金supported by the Academic Research Projects of Beijing Union University(ZK20202204)the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052)+1 种基金the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102)the Chinese Meridian Project(CMP).
文摘The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
文摘Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
基金supported by the National Natural Science Foundation of China(Grant Nos.42375062 and 42275158)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)the Natural Science Foundation of Gansu Province(Grant No.22JR5RF1080)。
文摘It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
基金the Young Investigator Group“Artificial Intelligence for Probabilistic Weather Forecasting”funded by the Vector Stiftungfunding from the Federal Ministry of Education and Research(BMBF)and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments。
文摘Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
基金supported by the National Natural Science Foundation of China(No.42377098)the National Key Research and Development Program of China(No.2022YFC3701100).
文摘Deep learning methods are increasingly employed to forecast air quality from an everincreasing stream of data and algorithms.However,the efficacy of current approaches may be questionable when evaluated not solely in terms of greater forecasting fidelity,but also concerning the decision-making process in pollution early warning.Here,rather than amending classical machine learning algorithms,we argue that now is the time to push the frontiers of air pollutant forecasting beyond state-of-the-art approaches.This can be achieved through near real-time assimilation of multiscale observations for laying the foundation of training data,enhanced attribution methods for impending heavy pollution,diagnostics for forecasting uncertainty,and advanced climate-chemistry emulators for improving seasonal forecasting.To harness this potential,it is essential to address several key challenges in deep learning methods,particularly generalization ability in extreme events,physics-informed interpretable approaches,and the mitigation technology of cumulative errors in multi-process coupled systems.This interdisciplinary endeavor will remain a central pursuit in the quest to anticipate and manage environmental change.
基金supported by European Union’s Horizon Europe Research and Innovation Programme(101092043)project AGILEHAND(Smart Grading,Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines).
文摘Dear Editor,The food industry emphasizes improving demand forecasting to align production with consumer needs and reduce waste.This letter thus presents a study that integrates artificial intelligence(AI)and digital twin(DT)technologies to enhance decision-making and efficiency in food production.A data-driven DT was implemented in an Italian company for Raspberry production planning,based on a daily demand forecasting tool powered by a dynamic extreme gradient boosting(XGBoost)algorithm.The model achieved a mean absolute percentage error(MAPE)of 16.37%with 1.69 average of absolute extra working hours(AEW)and a tracking signal(TS)range of[−1.9,+4.3].
基金supported by the funding from the Humanities and Social Science Fund of Ministry of Education of China(No.22YJCZH028)National Natural Science Foundation of China(Grant No.72303001)+3 种基金Fundamental Research Funds for the Central Universities(No.JUSRP124043)Anhui Provincial Excellent Young Scientists Fund for Universities(No.2024AH030001)Anhui Education Department Excellent Young Teachers Fund(No.YQYB2024021)Basic Research Program of Jiangsu(No.BK20251593)。
文摘The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.
文摘The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
基金supported by the open foundation of State Key Laboratory of Chemical Engineering(SKL-ChE-22B01)the Natural Science Foundation of China(22008169).
文摘The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this study proposes an innovative hybrid approach that integrates wavelet neural network(WNN)with chaos analysis.By leveraging the Cross-Correlation(C−C)method,the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN.Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals,advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators.Moreover,this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.
基金ANID for supporting the Fondecyt project 1200555.
文摘Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.
文摘In this study,we examine the connectedness between the NASDAQ artificial intelligence index and sectoral cryptocurrency indices.Empirical analyses were conducted via the quantile‒quantile methodology and cross-multiquantilogram tests across 15 cryptocurrency sectors from June 1,2021,to May 28,2024.The results show that dynamic total spillovers primarily occur in extremely low and high quantiles,corresponding to the left and right tails of the return distributions.Net directional spillovers indicate the dominance of the AI sector over the cryptocurrency market,which intensifies during significant crashes or booms.The most substantial effect of AI is observed in the DeFi,NFT,and Smart Contracts sectors,highlighting the prominence of financial operation-based blockchain applications in their interaction with artificial intelligence.The cross-multiquantilogram results also suggest that developments in artificial intelligence dominate the cryptocurrency market and have high predictability in its price movements.On the basis of our findings,we recommend using the AI market as an early indicator for the cryptocurrency market and advise against combining these two asset groups in the same portfolio to maintain diversification benefits.
文摘Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.
基金jointly supported by the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 316323005]the Guangdong Basic and Applied Basic Research Foundation[grant numbers 2023A1515010741 and 2024B1515020035]the Science and Technology Planning Project of Guangdong Province[grant number 2023B1212060019]。
文摘Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.
文摘Based on ground observation data of relative humidity,the prediction performance of STNF and MIFS in each competition area during February 13-26,2024 was tested and evaluated by using two intelligent forecasting methods(STNF and MIFS).The results show that STNF had better performance in forecasting relative humidity in high-altitude areas,and was suitable for fine forecasting under complex terrain.MIFS improved the short-term forecast of some low-altitude stations,but the long-term reliability was insufficient.STNF method performed better than MIFS during 0-24 h.As the prediction time extended to 24-72 h,the errors of both methods showed a systematic increase trend.STNF had higher precision,lower root mean square error and smaller mean error in most regions under the background of most weather systems,showing its superiority as a forecasting method of relative humidity.However,the precision of MIFS was slightly higher than that of STNF in Liangcheng without system background,revealing that MIFS may also be an effective option in some specific conditions.
基金financial support from the National Natural Science Foundation of China(Grant Nos.71971079,72271087,and 71871088)the Major Projects of the National Social Science Foundation of China(Grant No.21ZDA114)+1 种基金the National Social Science Foundation of China(Grant No.19BTJ018)the Hunan Provincial Natural Science Foundation of China(Grant No.21JJ20019).
文摘Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.
