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The interpretability of AI is just as important as its performance. In the LegalAI field, there have been efforts to enhance the interpretability of models, but a trade-off between interpretability and prediction accuracy remains inevitable. In this paper, we introduce a novel framework called LK-IB for compulsory measure prediction (CMP), one of the critical tasks in LegalAI. LK-IB leverages Legal Knowledge and combines an Interpretable model and a Black-box model to balance interpretability and prediction performance. Specifically, LK-IB involves three steps: (1) inputting cases into the first module, where first-order logic (FOL) rules are used to make predictions and output them directly if possible; (2) sending cases to the second module if FOL rules are not applicable, where a case distributor categorizes them as either "simple" or "complex"; and (3) sending simple cases to an interpretable model with strong interpretability and complex cases to a black-box model with outstanding performance. Experimental results demonstrate that the LK-IB framework provides more interpretable and accurate predictions than other state-of-the-art models. Given that the majority of cases in LegalAI are simple, the idea of model combination has significant potential for practical applications. [ABSTRACT FROM AUTHOR]

The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t-distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions. [ABSTRACT FROM AUTHOR]

The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specialised frameworks, has led to a growth in interest and a need for new algorithms able to deal with large-scale datasets and techniques to adapt traditional machine learning algorithms to this new paradigm. This paper presents several ensemble alternatives, based on model selection and combination, that allow for obtaining a continuous piecewise linear regression model from large-scale datasets using the learning algorithm of the LHM. Our empirical tests have proved that model combination outperforms model selection and that these methods can provide better results in terms of bias, variance, and execution time than the original algorithm executed over the entire dataset. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change has led to extreme weather events and food security issues, which have become urgent global problems. Crop models and hydrological models play important roles in analyzing these issues, but crop models have limitations in large-scale simulations, and hydrological models have shortcomings in depicting underlying surface details. Jointly using the two models to complement each other's shortcomings is becoming an important method, playing an important role in improving the quantitative characterization ability of different facets of agricultural production and is crucial to improving the efficiency of water resources utilization. This paper reviews the development process of crop and hydrological models and summarizes the widely used models at present, providing reference for researchers to select crop and hydrological models; secondly, it summarizes the types, methods, and examples of joint use of models, and analyzes possible problems in model combination; finally, some suggestions are put forward for the joint use of crop and hydrological models, providing a reference for researchers to combine models.

For some engineering application, accurately estimating reliability only depend on the history data or failure mechanism is difficult to implement, due to the lack of data and imperfect theory of failure mechanism. Namely, both history data and failure mechanism should be utilized to improve the reliability estimation accuracy for engineering applications. Hence, we construct a reliability estimation method by fusing the failure mechanism and artificial neural network (ANN) supported Wiener processes for utilizing both history data and failure mechanism. ANN and failure mechanism are integrated into Wiener process with random effects, respectively. Bayesian model averaging (BMA) method is adapted to combine the failure mechanism with ANN supported Wiener processes, as well as to update the model parameters by fusing data. Based on a typical aviation hydraulic pump's actual dataset, we illustrate the advantages of our approach by comparing to Wiener process supported only by ANN or failure mechanism in engineering practices. The proposed method shows superiorities on reliability estimation considering the estimation accuracies comparing the other two models.

Traditional uncertainty analysis for subsurface models is typically based on a single dynamic model with a number of uncertain parameters. Improved and more robust forecasting can be obtained by combining several models in a Bayesian setting using model averaging. The traditional Bayesian Model Averaging (BMA), however, suffers from several drawbacks, such as too large sensitivity to prior model assumptions and instability with respect to measurement perturbations, especially when the number of measurements is large. We suggest a modified version of BMA (MBMA) where the calculations are stabilized using an ensemble of measurements. Bayesian stacking (BS) is a method that is directly focused on the performance of the combined predictive distribution of several models. The original version of BS (BSLOO) is based on leave-one-out cross-validation and requires a Bayesian inversion for each data point which may be very time consuming. We suggest a modified version of stacking (MBS) that requires only a single history match and uses an ensemble of measurements. MBS may be used with either prior (MBS-pri) or posterior (MBS-post) predictive distributions. The behavior of the methods is illustrated using three synthetic, linear examples. One is a simple mixture model. The other two are inspired by 4D seismic data. The results with MBS-pri are very similar to the results with MBMA. The results with MBS-post are similar to those of BSLOO when the data are uncorrelated. MBS can take into account correlated data or measurement errors, while correlations are neglected in the BSLOO weight calculations. [ABSTRACT FROM AUTHOR]

Climate change is expected to increase the frequency and intensity of extreme weather events. To properly assess the increased economical risk of these events, actuaries can gain in relying on expert models/opinions from multiple different sources, which requires the use of model combination techniques. From non-parametric to Bayesian approaches, different methods rely on varying assumptions potentially leading to very different results. In this paper, we apply multiple model combination methods to an ensemble of 24 experts in a pooling approach and use the differences in outputs from the different combinations to illustrate how one can gain additional insight from using multiple methods. The densities obtained from pooling in Montreal and Quebec City highlight the significant changes in higher quantiles obtained through different combination approaches. Areal reduction factor and quantile projected changes are used to show that consistency, or lack thereof, across approaches reflects the uncertainty of combination methods. This shows how an actuary using multiple expert models should consider more than one combination method to properly assess the impact of climate change on loss distributions, seeing as a single method can lead to overconfidence in projections. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-level dynamic factor model to identify common components in output gap estimates. We pool multiple estimates for 157 countries and decompose them into one global, eight regional, and 157 country-specific cycles. Our approach easily deals with mixed frequencies, ragged edges, and discontinuities in the underlying output gap estimates. To restrict the parameter space in the Bayesian state space model, we apply a stochastic search variable selection approach and base the prior inclusion probabilities on spatial information. Our results suggest that the global and the regional cycles explain a substantial proportion of the output gaps. On average, 18% of a country's output gap is attributable to the global cycle, 24% to the regional cycle, and 58% to the local cycle. [ABSTRACT FROM AUTHOR]

Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes. [ABSTRACT FROM AUTHOR]

The stock market forecast is an important and challenging issue. Its distribution forecast of returns can provide information that is more complete, compared to point forecast and interval forecast. As intraday high-frequency information is available, we incorporate intraday returns into the predictive modelling of daily return distribution in two ways: realized volatility and scale calibration. Three parametric models, EGARCH, EGARCH-X, and realGARCH, and two nonparametric models, NP and realNP, are used. Our improved NP model, the realNP model, is based on intraday returns calibration. The results show that intraday information improves goodness-of-fit and forecasting effect, and the realGARCH model is relatively the best. According to the realNP model results, the intraday returns can only contribute about a 30% description of the daily distribution and less than 1% information for a one-step-ahead distribution forecast. Furthermore, three combinations are considered, and the log-score and CRPS combinations are found to have direction predictability and excess profitability statistically. The non-short-selling situation consistently has more excess profits than the short-selling situation, which implies that the non-short-selling rule protects investors. This study reveals the importance of incorporating intraday information and model combinations for stock market forecast modelling. [ABSTRACT FROM AUTHOR]

Based on the load characteristics of industrial parks, this paper optimizes the load prediction model of industrial parks, in order to provide data support for the research of scheduling algorithms. Aiming at the influence of Variational Modal Decomposition (VMD) modal parameters K and penalty factor α on the prediction accuracy of short-term power load forecasting method based on variational modal decomposition (VMD) and Gated Recurrent Unit (GRU), Whale Optimization Algorithm (WOA) was proposed. In this paper, WOA is used to optimize VMD decomposition parameters. Then, the optimized decomposition parameters decompose the original load data, and a set of more regular modal components are obtained. Finally, each mode decomposed by the WOA-VMD algorithm was sent to GRU for power prediction. And the prediction results were superimposed and reconstructed to obtain the final result. WOA optimized the search process and found more appropriate parameters for better prediction results. After whale algorithm optimization, Root Mean Square Error (RMSE) decreased from 108.8 MW to 38.29 MW, Mean Absolute Error (MAE) decreased from 83.09 MW to 24.26 MW.

Effective approaches to forecast model selection are crucial to improve forecast accuracy and to facilitate the use of forecasts for decision-making processes. Information criteria or cross-validation are common approaches of forecast model selection. Both methods compare forecasts with the respective actual realizations. However, no existing selection method assesses out-of-sample forecasts before the actual values become available—a technique used in human judgment in this context. Research in judgmental model selection emphasizes that human judgment can be superior to statistical selection procedures in evaluating the quality of forecasting models. We, therefore, propose a new way of statistical model selection based on these insights from human judgment. Our approach relies on an asynchronous comparison of forecasts and actual values, allowing for an ex ante evaluation of forecasts via representativeness. We test this criterion on numerous time series. Results from our analyses provide evidence that forecast performance can be improved when models are selected based on their representativeness. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4485. [ABSTRACT FROM AUTHOR]

The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specialised frameworks, has led to a growth in interest and a need for new algorithms able to deal with large-scale datasets and techniques to adapt traditional machine learning algorithms to this new paradigm. This paper presents several ensemble alternatives, based on model selection and combination, that allow for obtaining a continuous piecewise linear regression model from large-scale datasets using the learning algorithm of the LHM. Our empirical tests have proved that model combination outperforms model selection and that these methods can provide better results in terms of bias, variance, and execution time than the original algorithm executed over the entire dataset.

Groundwater is an essential source to supply water for various sectors. This paper aimed to predict the quantitative and qualitative changes in groundwater over time and to evaluate the efficiency of different modeling methods. This study is based on three steps. In the first step, quantitative and qualitative piezometers were clustered by the Growing Neural Gas Network (GNG) method, and the central piezometer of each cluster was used on behalf of each cluster. In the second step, four different Artificial Intelligence (AI) models were applied, namely Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Emotional Artificial Neural Network (EANN). As a post-processing approach three different ensemble methods were used: simple average ensemble (SAE), weighted average ensemble (WAE), and nonlinear neural network ensemble (NNE). In the third step, the outputs of single AI models were used to enhance the evaluation results. Therefore, the results demonstrate that the NNE led to reach the better performance for three GWL, TDS, and TH parameters up to 37, 29, and 23% on average, respectively. Study results will lead to the improvement of AI applications in groundwater research and will benefit groundwater development plans. HIGHLIGHTS Artificial intelligence methods were used to predict quantitative and qualitative changes in groundwater.; The GNG method was used for clustering.; Ensemble artificial intelligence-based modeling was employed to enhance the individual modeling results.; The results show better performance when using ensemble artificial intelligence-based modeling.;

Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.

Water-related issues are becoming more widespread due to the increased frequency and severity of extreme climatic events. Under such a scenario, accurate streamflow prediction is necessary both during wet and dry periods. Most hydrological models developed so far focus on simulating high flows and thus perform poorly during dry or recession periods. Several studies in the past, therefore, resorted to calibration techniques to enable rainfall-runoff (R-R) models better capture recession flow dynamics. In the present study, a model combination approach is proposed, which utilises the relative strengths of two structurally different models to improve overall streamflow prediction. In particular, the proposed framework combines a conceptual rainfall-runoff model (HBV) with a power-law regression (PLR) model such that the former is used for high flow prediction and the latter for low flow prediction. The proposed framework (HBV-PLR) is evaluated in 108 basins in the United States, and its performance is compared with the original HBV model. The results show that the 25th, 50th, and 75th percentiles of mean absolute error (MAE), which were (0.47, 0.62, and 0.77), respectively, for the HBV, improved to (0.38, 0.50, and 0.67) using the HBV-PLR combination. Similarly, the median Nash-Sutcliffe Efficiency (NSE) during the recession improved from 0.65 to 0.74. In this study, we also demonstrated that the HBV, even being calibrated using an objective function biased towards lower values, may not predict low flows as precisely as the HBV-PLR. Our results suggest that a model combination approach may be a better option than a single model. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.

Scientific advice to the UK government throughout the COVID-19 pandemic has been informed by ensembles of epidemiological models provided by members of the Scientific Pandemic Influenza group on Modelling. Among other applications, the model ensembles have been used to forecast daily incidence, deaths and hospitalizations. The models differ in approach (e.g. deterministic or agent-based) and in assumptions made about the disease and population. These differences capture genuine uncertainty in the understanding of disease dynamics and in the choice of simplifying assumptions underpinning the model. Although analyses of multi-model ensembles can be logistically challenging when time-frames are short, accounting for structural uncertainty can improve accuracy and reduce the risk of over-confidence in predictions. In this study, we compare the performance of various ensemble methods to combine short-term (14-day) COVID-19 forecasts within the context of the pandemic response. We address practical issues around the availability of model predictions and make some initial proposals to address the shortcomings of standard methods in this challenging situation. [ABSTRACT FROM AUTHOR]

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches were used to predict the effective reproduction number, R(t), and other COVID-19-related measures such as the daily rate of exponential growth, r(t). These candidate models use different modelling approaches or differing assumptions about spatial or age-mixing, and some capture genuine uncertainty in scientific understanding of disease dynamics. Combining estimates using appropriate statistical methodology from multiple candidate models is important to better understand the variation of these outcome measures to help inform decision-making. In this paper, we combine estimates for specific UK nations/regions using random-effects meta-analyses techniques, utilising the restricted maximum-likelihood (REML) method to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type and the Knapp and Hartung (KNHA) method. As estimates in this setting are derived using model predictions, each with varying degrees of uncertainty, equal-weighting is favoured over the standard inverse-variance weighting to avoid potential up-weighting of models providing estimates with lower levels of uncertainty that are not fully accounting for inherent uncertainties. Both equally-weighted models using REML alone and REML+KNHA approaches were found to provide similar variation for R(t) and r(t), with both approaches providing wider, and therefore more conservative, confidence intervals around the combined estimate compared to the standard inverse-variance weighting approach. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures. This in turn allows timely and informed decision-making based on all available information. [ABSTRACT FROM AUTHOR]

Many alternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. We propose and assess a stacked regression ensemble that optimally combines different mortality models to reduce out-of-sample mean squared errors and mitigate model selection risk. Stacked regression uses a meta-learner to approximate horizon-specific weights by minimizing a cross-validation criterion for each forecasting horizon. The horizon-specific weights determine a mortality model combination customized to each horizon. We use 44 populations from the Human Mortality Database to compare the stacked regression ensemble with alternative methods. We show that, using one-year-ahead to 15-year-ahead out-of-sample mean squared errors, the stacked regression ensemble improves mortality forecast accuracy by 13% - 49% for males and 19% - 90% for females over individual mortality models. The stacked regression ensembles also have better predictive accuracy than other model combination methods, including Simple Model Averaging, Bayesian Model Averaging, and Model Confidence Set. We provide an R package, CoMoMo, that combines forecasts for Generalized-Age-Period-Cohort models. [ABSTRACT FROM AUTHOR]

In data-driven modeling of complex dynamic processes, it is often desirable to combine different classes of models to enhance performance. Examples include coupled models of different fidelities, or hybrid models based on physical knowledge and data-driven strategies. A key limitation of the broad adoption of model combination in applications is intrusiveness: training combined models typically requires significant modifications to the learning algorithm implementations, which may often be already well-developed and optimized for individual model spaces. In this work, we propose an iterative, non-intrusive methodology to combine two model spaces to learn dynamics from data. We show that this can be understood, at least in the linear setting, as finding the optimal solution in the direct sum of the two hypothesis spaces, while leveraging only the projection operators in each individual space. Hence, the proposed algorithm can be viewed as iterative projections, for which we can obtain estimates on its convergence properties. To highlight the extensive applicability of our framework, we conduct numerical experiments in various problem settings, with particular emphasis on various hybrid models based on the Koopman operator approach. • We introduce a non-intrusive way to combine models for learning dynamics. • We prove that our method converges linearly when the hypothesis spaces are closed linear spaces. • We show that our method can be applied to a variety of prediction and control problems. [ABSTRACT FROM AUTHOR]

Recently, Liu and Zhang studied the rather challenging problem of time series forecasting from the perspective of compressed sensing. They proposed a no-learning method, named Convolution Nuclear Norm Minimization (CNNM), and proved that CNNM can exactly recover the future part of a series from its observed part, provided that the series is convolutionally low-rank. While impressive, the convolutional low-rankness condition may not be satisfied whenever the series is far from being seasonal, and is in fact brittle to the presence of trends and dynamics. This paper tries to approach the issues by integrating a learnable, orthonormal transformation into CNNM, with the purpose for converting the series of involute structures into regular signals of convolutionally low-rank. We prove that the resultant model, termed Learning-Based CNNM (LbCNNM), strictly succeeds in identifying the future part of a series, as long as the transform of the series is convolutionally low-rank. To learn proper transformations that may meet the required success conditions, we devise an interpretable method based on Principal Component Pursuit (PCP). Equipped with this learning method and some elaborate data argumentation skills, LbCNNM not only can handle well the major components of time series (including trends, seasonality and dynamics), but also can make use of the forecasts provided by some other forecasting methods; this means LbCNNM can be used as a general tool for model combination. Extensive experiments on 100,452 real-world time series from Time Series Data Library (TSDL) and M4 Competition (M4) demonstrate the superior performance of LbCNNM. [ABSTRACT FROM AUTHOR]

Our work aims to evaluate two strategies to forecast industrial output growth, one of the important macroeconomic indicators for a country. Automatic algorithms that require few or no human interventions and are neutral generate good forecast performance and can be helpful for policymakers. We propose an automatic algorithm that is based on cross-validating different models and finding an optimal combination of them as soon as a new information is available. We evaluate two strategies. The first strategy aims to improve the Granger and Bates method by using cross-validation to better estimate the weights that are used in forecast combinations. The second strategy selects the best model to be used for forecasting based on the Cv technique (CvML). We apply our strategies to the industrial output growth rate data of six countries: Brazil, Germany, the United States, France, Japan, and the United Kingdom. Both cited methods were applied on "pseudo" streaming data, with observations feeding the model one by one, being re-estimated after each step, with an automatic selection/combination of models. Our results show that CvML outperforms all other benchmark models in most cases, especially in the long run. Even when CvML is not the best performing model, it has the same statistical performance as the best one. [ABSTRACT FROM AUTHOR]

Climate change has led to extreme weather events and food security issues, which have become urgent global problems. Crop models and hydrological models play important roles in analyzing these issues, but crop models have limitations in large-scale simulations, and hydrological models have shortcomings in depicting underlying surface details. Jointly using the two models to complement each other's shortcomings is becoming an important method, playing an important role in improving the quantitative characterization ability of different facets of agricultural production and is crucial to improving the efficiency of water resources utilization. This paper reviews the development process of crop and hydrological models and summarizes the widely used models at present, providing reference for researchers to select crop and hydrological models; secondly, it summarizes the types, methods, and examples of joint use of models, and analyzes possible problems in model combination; finally, some suggestions are put forward for the joint use of crop and hydrological models, providing a reference for researchers to combine models. • Retrospect on the development process of crop and hydrological models. • Summarized case studies on the combination of crop and hydrological models. • Provide an overview of cases where crop and hydrological models are used in conjunction. • General considerations and suggestions for future development. [ABSTRACT FROM AUTHOR]

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, an analyst needs to appropriately fine-tune their strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact on such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategy calibration and aggregation. To this end, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategy calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategy calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha. [ABSTRACT FROM AUTHOR]

Stacking is a model combination technique to improve prediction accuracy. Regularization is usually necessary in stacking because some predictions used in the model combination provide similar predictions. Cross-validation is generally used to select the regularization parameter, but it incurs a high computational cost. This paper proposes two simple low computational cost methods for selecting the regularization parameter. The effectiveness of the methods is examined in numerical experiments. Asymptotic results in a particular setting are also shown. [ABSTRACT FROM AUTHOR]

In this paper, we explore the hedging performance of CSI 300 stock index futures under the minimum-variance and maximum-utility framework. We employ ten commonly used econometric models including constant and dynamic ones. Our empirical results indicate that for all futures contracts none of the single model can outperform all other models out-of-sample. In general, the dynamic hedge ratio models are significantly superior to the constant hedge ratio models under the both frameworks in CSI 300 market. Further we obtain the most robust hedging effectiveness by combining OHRs from dynamic hedge models. [ABSTRACT FROM AUTHOR]

Future changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre‐processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950–1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance‐based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi‐model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias‐correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (>0.5). Future (2000–49) model combined projections exhibit an increase in the mean seasonal temperature by 2°C for winter and by 1°C for summer over almost all climate regions. [ABSTRACT FROM AUTHOR]

The estimation of the PM2.5 concentration based on satellite remote sensing is currently a hot topic for research, with machine learning algorithms starting to be applied to the estimation model. However, there are few comparisons between different machine learning algorithms, and few scholars objectively evaluate the estimation performance of machine learning algorithms under different weather conditions. In this study, PM2.5 concentration was estimated by six commonly used machine learning algorithms, and their performances were compared in four different weather conditions in the Beijing–Tianjin–Hebei (BTH) region. The results showed that decision tree and random forest consistently performed well in different weather conditions, while SVM performed poorly. When the PM2.5 concentration was greater than 150 μg/m3, the R and RMSE values for decision tree were 0.854 and 31.53 μg/m3, respectively, while the evaluation coefficient of SVM algorithm was only 0.597 and 49.31 μg/m3, it was worth noting that all algorithms performed better in this interval than in others. This study also focused on the development of an optimal combination algorithm to estimate PM2.5 concentration under different weather conditions and got a good application effect. The results of this research may provide a theoretical basis and an important reference for the application of machine learning algorithms in the field of remote sensing. [ABSTRACT FROM AUTHOR]

Model averaging for forecasting and mixed estimation is a recognized improved statistical approach. This paper is a first report on: (1). aggregate information from different conditional quantiles within a given model and, (2). model averaging with quantile averaging. Based on a subset of possible methods, we show that aggregating information over different quantiles, with and without combining information across different models, can produce superior forecasts, outperforming forecasts based on conditional mean regressions. We observe a variety of quantile aggregation schemes within a model can significantly improve over forecasts obtained from model combination alone. We provide simulation and empirical evidence. In addition economic value of our proposals is demonstrated within an optimal portfolio decision setting. Higher values of average utility are observed with no exception when an investor employs forecasts which aggregate both within and across model information. [ABSTRACT FROM AUTHOR]

No consensus exists in the literature on whether stock prices can be predicted, with most existing studies employing point forecasting to predict returns. By contrast, this study adopts the new perspective of distribution forecasting to investigate the predictability of the stock market using the model combination strategy. Specifically, the Shanghai Composite Index and the Shenzhen Component Index are selected as research objects. Seven models – GARCH-norm, GARCH-sstd, EGARCH-sstd, EGARCH-sstd-M, one-component Beta-t-EGARCH, two-component Beta-t-EGARCH, and the EWMA-based nonparametric model – are employed to perform distribution forecasting of the returns. The results of out-of-sample forecasting evaluation show that none of the individual models is "qualified" in terms of predictive power. Therefore, three combinations of individual models were constructed: equal weight combination, log-likelihood score combination, and continuous ranked probability score combination. The latter two combinations were found to always have significant directional predictability and excess profitability, which indicates that the two combined models may be closer to the real data generation process; from the perspective of economic evaluation, they may have a predictive effect on the conditional return distribution in China's stock market. [ABSTRACT FROM AUTHOR]

This study aimed at time-space estimations of monthly precipitation via a two-stage modeling framework. In temporal modeling as the first stage, three different Artificial Intelligence (AI) models were applied to observed precipitation data from 7 gauges located at Northern Cyprus. In this way 2 different input scenarios proposed, by employing different input combinations. Afterwards, the outputs of single AI models were used to generate ensemble techniques to enhance the precision of modeling by the single AI models. For this purpose, 2 linear and 1 non-linear methods of ensembling were designed and afterwards, the results were evaluated. In the second stage, for estimation of the spatial distribution of precipitation over whole region, the results of temporal modeling were used as inputs for the Inverse Distance Weighting (IDW) spatial interpolator. The cross-validation was finally applied to evaluate the overall accuracy of the proposed hybrid spatiotemporal modeling approach. The obtained results in temporal modeling stage demonstrated that the non-linear ensemble technique provided more accurate results. Results of spatial modeling stage indicated that IDW scheme is a good choice for spatial estimation of the precipitation. The overall results show that the combination of temporal and spatial modeling tools could simulate the precipitation appropriately by serving unique features of both tools. [ABSTRACT FROM AUTHOR]

For some engineering application, accurately estimating reliability only depend on the history data or failure mechanism is difficult to implement, due to the lack of data and imperfect theory of failure mechanism. Namely, both history data and failure mechanism should be utilized to improve the reliability estimation accuracy for engineering applications. Hence, we construct a reliability estimation method by fusing the failure mechanism and artificial neural network (ANN) supported Wiener processes for utilizing both history data and failure mechanism. ANN and failure mechanism are integrated into Wiener process with random effects, respectively. Bayesian model averaging (BMA) method is adapted to combine the failure mechanism with ANN supported Wiener processes, as well as to update the model parameters by fusing data. Based on a typical aviation hydraulic pump's actual dataset, we illustrate the advantages of our approach by comparing to Wiener process supported only by ANN or failure mechanism in engineering practices. The proposed method shows superiorities on reliability estimation considering the estimation accuracies comparing the other two models., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Di Liu reports financial support was provided by State Key Laboratory of Fluid Power and Mechatronic Systems. Di Liu reports was provided by Tianmushan Laboratory. Di Liu reports financial support was provided by Key Laboratory of Flight Techniques and Flight Safety. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)