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1.
考虑BP网络存在收敛速度慢、局部极值等缺点,引入线性下降惯性权重粒子群优化（LWPSO）算法,建立基于线性下降惯性权重粒子群优化（LWPSO）算法的人工神经网络模型,在分析抚顺发电有限责任公司厂区地表下沉的实际观测资料的基础上,对厂区的任意点,任意时刻进沉陷预测研究。 相似文献
2.
Partha Sengupta;Christopher H. Wheeler; 《Journal of forecasting》2024,43(7):2448-2477
Models developed by banks to forecast losses in their credit card portfolios have generally performed poorly during the COVID-19 pandemic, particularly in 2020, when large forecast errors were observed at many banks. In this study, we attempt to understand the source of this error and explore ways to improve model fit. We use account-level monthly performance data from the largest credit card banks in the U.S. between 2008 and 2018 to build models that mimic the typical model design employed by large banks to forecast credit card losses. We then fit these on data from 2019 to 2021. We find that COVID-period model errors can be reduced significantly through two simple modifications: (1) including measures of the macroeconomic environment beyond indicators of the labor market, which served as the primary macro drivers used in many pre-pandemic models and (2) adjusting macro drivers to capture persistent/sustained changes, as opposed to temporary volatility in these variables. These model improvements, we find, can be achieved without a significant reduction in model performance for the pre-COVID period, including the Great Recession. Moreover, in broadening the set of macro influences and capturing sustained changes, we believe models can be made more robust to future downturns, which may bear little resemblance to past recessions. 相似文献
3.
Jiaming Liu;Jiajia Liu;Chong Wu;Shouyang Wang; 《Journal of forecasting》2024,43(2):429-455
The assessment of credit risk for P2P lending platform applicants is critical to investors. Feature engineering is an essential technique in distilling classification knowledge during the credit risk prediction data preprocessing stage. Although previous literature used feature selection methods to identify key features, feature transformation is more useful in discovering intrinsic nonlinear characteristics in credit data. In this study, we propose a synthetic multiple tree-based feature transformation method to generate features. Multiple tree-based feature transformation methods are employed and fused to acquire a new feature set. The bagging-based tree ensemble feature transformation method (Bagging-TreeEnsembleFT) and boosting-based tree ensemble feature transformation method (Boosting-TreeEnsembleFT) are two types of feature transformation methods that we specifically propose to validate their effect. We verify the credit risk prediction performance using the proposed synthetic feature transformation methods on real P2P Lending credit datasets. Empirical analysis demonstrates that tree-based ensemble feature transformation methods with boosting ensemble strategy achieve better prediction performance on various datasets corresponding to different partitions and class distributions compared to tree-based ensemble feature transformation methods with bagging ensemble strategy and individuals. Moreover, the proposed synthetic feature transformation method improves the credit risk prediction performance in terms of accuracy, AUC, and F1-score. 相似文献
4.
Reliable photovoltaic and wind power generation forecasts are essential for efficient power systems operations. A combined forecasting system is developed, which integrates a data preprocessing method, a sub-predictor selection rule, and a multi-objective optimization to integrate various forecasting models. The proposed system effectively aggregates the advantages of all algorithms involved, facilitating greater prediction precision and stability. Experiments indicated that the proposed system can achieve higher quality point and interval forecasting performance relative to the comparative approaches. 相似文献
5.
Cristina Danciulescu 《Journal of forecasting》2016,35(4):285-307
This paper proposes the implementation of a VaR backtesting procedure able to overcome the subadditivity property failure of value‐at‐risk (VaR). More precisely, we propose the implementation of a multivariate portmanteau test statistic of Ljung–Box type applied to hits collected from several trading desks or divisions at once. Simulation exercises illustrate that this method is testing for aggregate risk, accurately accounting both for diversification (negative hit cross‐correlation) and contagion/risk spillovers (positive hit cross‐correlation). An application using profit and loss and VaR data collected for two international major banks illustrates how our proposed backtesting procedure performs in a realistic environment. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
6.
Peer-to-peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision-making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real-world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations. 相似文献
7.
In this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameters. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
8.
Ying Zhou;Xia Lin;Guotai Chi;Peng Jin;Mengtong Li; 《Journal of forecasting》2024,43(3):615-643
This study aims to solve the imbalanced sample problem in default prediction. We calculate the classification contribution score of each default customer by the entropy weight technique (EWT) for order of preference by similarity to the ideal solution and construct a default prediction model according to several models. Our proposed EWT-synthetic minority oversampling technique (SMOTE) method significantly improves the prediction accuracy of several typical default prediction models and reduces type II error. We find that the indicators “net cash flow from operating activities,” “Engel coefficient,” “basic earnings per share,” and “total social retail sales” significantly influence default prediction of Chinese listed companies. 相似文献
9.
Michał Thor;Łukasz Postek; 《Journal of forecasting》2024,43(5):1131-1152
This paper presents a promising approach using gated recurrent unit (GRU) network to predict bankruptcy based on the whole sequence of financial statements of the companies listed on an unregulated market. This approach contrasts with the traditional literature where default prediction is usually tackled with methods that do not fully account for a company's history. The GRU network can be used to model long-term dependencies thanks to its update and reset gates, which prevent the vanishing gradient problem and decide how much of the past information is relevant for predicting a default. This aspect may be of utmost importance in alternative markets where the signal detection problem is particularly strong. The performance of the GRU network is compared against the performance of other standard machine learning methods including Cox proportional hazards, gradient-boosted Cox proportional hazards model, extreme gradient boosting, random survival forest, and standard recurrent neural networks on the basis of a broad selection of performance metrics. The GRU network not only outperforms standard machine learning methods in out-of-sample forecasts but also seems to be more robust in terms of in- versus out-of-sample performance. 相似文献
10.
Dag Kolsrud 《Journal of forecasting》2015,34(8):675-693
A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
11.
Many applications in science involve finding estimates of unobserved variables from observed data, by combining model predictions with observations. The sequential Monte Carlo (SMC) is a well‐established technique for estimating the distribution of unobserved variables that are conditional on current observations. While the SMC is very successful at estimating the first central moments, estimating the extreme quantiles of a distribution via the current SMC methods is computationally very expensive. The purpose of this paper is to develop a new framework using probability distortion. We use an SMC with distorted weights in order to make computationally efficient inferences about tail probabilities of future interest rates using the Cox–Ingersoll–Ross (CIR) model, as well as with an observed yield curve. We show that the proposed method yields acceptable estimates about tail quantiles at a fraction of the computational cost of the full Monte Carlo. 相似文献
12.
Yang Liu;Fei Huang;Lili Ma;Qingguo Zeng;Jiale Shi; 《Journal of forecasting》2024,43(2):286-308
Credit scoring models based on machine learning often need to work on accuracy and interpretability in practical applications. Original KCDWU has a more prominent adaptive property but ignores intra-class and inter-class distances in the clustering process, resulting in the possibility of inaccurate identification of class features and cluster structure of data, which compromises the clustering effect. Therefore, we improve the automatic K-means clustering based on the Calinski–Harabasz index, thus achieving a clustering output for improved results. We also scrutinize representative five single classification models and six ensemble learning models for credit scoring prediction. We empirically test the superior performance of ensemble learning models and identify the best model CatBoost by comparing them based on multiple evaluation indicators. Empirical results reveal that the SHAP method conforms well to CatBoost and delivers a global and local interpretation of the predictions. This work provides financial institutions with a promising candidate for interpretable credit scoring models. 相似文献
13.
Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high‐profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non‐parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
14.
M. A. Kaboudan 《Journal of forecasting》1999,18(5):345-357
Based on the standard genetic programming (GP) paradigm, we introduce a new probability measure of time series' predictability. It is computed as a ratio of two fitness values (SSE) from GP runs. One value belongs to a subject series, while the other belongs to the same series after it is randomly shuffled. Theoretically, the boundaries of the measure are between zero and 100, where zero characterizes stochastic processes while 100 typifies predictable ones. To evaluate its performance, we first apply it to experimental data. It is then applied to eight Dow Jones stock returns. This measure may reduce model search space and produce more reliable forecast models. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献
15.
We describe a method for calculating simultaneous prediction intervals for ARMA times series with heavy‐tailed stable innovations. The spectral measure of the vector of prediction errors is shown to be discrete. Direct computation of high‐dimensional stable probabilities is not feasible, but we show that Monte Carlo estimates of the interval width is practical. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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17.
The purpose of this paper is to build an alternative method of bankruptcy prediction that accounts for some deficiencies in previous approaches that resulted in poor out‐of‐sample performances. Most of the traditional approaches suffer from restrictive presumptions and structural limitations and fail to reflect the panel properties of financial statements and/or the common macroeconomic influence. Extending the work of Shumway (2001), we present a duration model with time‐varying covariates and a baseline hazard function incorporating macroeconomic dependencies. Using the proposed model, we investigate how the hazard rates of listed companies in the Korea Stock Exchange (KSE) are affected by changes in the macroeconomic environment and by time‐varying covariate vectors that show unique financial characteristics of each company. We also investigate out‐of‐sample forecasting performances of the suggested model and demonstrate improvements produced by allowing temporal and macroeconomic dependencies. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
18.
Mehdi Divsalar Habib Roodsaz Farshad Vahdatinia Ghassem Norouzzadeh Amir Hossein Behrooz 《Journal of forecasting》2012,31(6):504-523
In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multi‐expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and non‐bankrupt Iranian corporations based on their financial ratios. An important contribution of this paper is to identify the effective predictive financial ratios on the basis of an extensive bankruptcy prediction literature review and upon a sequential feature selection analysis. The predictive performance of the GEP and MEP forecasting methods is compared with the performance of traditional statistical methods and a generalized regression neural network. The proposed GEP and MEP models are effectively capable of classifying bankrupt and non‐bankrupt firms and outperform the models developed using other methods. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
19.
Juanjuan Wang;Shujie Zhou;Wentong Liu;Lin Jiang; 《Journal of forecasting》2024,43(6):1998-2020
Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality-based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions. 相似文献
20.
针对底板破坏带的精度问题提出新的预计模型,通过搜集众多矿井的实测数据,应用多元统计分析算法,在支持向量机的基础上建立预计模型。采用果蝇优化算法对预计模型进行优化训练,建立FOA—SVM预计模型。利用实测数据对模型的预计结果进行检验,预计结果准确,比遗传算法模型、粒子群模型的预计结果稳定性更好和精度更高。 相似文献