共查询到20条相似文献，搜索用时 15 毫秒
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《International Journal of Forecasting》2022,38(4):1415-1425
This paper describes a deep-learning-based time-series forecasting method that was ranked third in the accuracy challenge of the M5 competition. We solved the problem using a deep-learning approach based on DeepAR, which is an auto-regressive recurrent network model conditioned on historical inputs. To address the intermittent and irregular characteristics of sales demand, we modified the training procedure of DeepAR; instead of using actual values for the historical inputs, our model uses values sampled from a trained distribution and feeds them to the network as past values. We obtained the final result using an ensemble of multiple models to make a robust and stable prediction. To appropriately select a model for the ensemble, each model was evaluated using the average weighted root mean squared scaled error, calculated for all levels of a wide range of past periods. 相似文献
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首先介绍了统计过程控制(简称SPC)的概念,并分析了其在过程质量控制中的重要作用。接着着重阐述了SPC的核心监控工具——控制图及其使用程序。最后提出了企业在实施SPC技术应遵从的步骤以及在实施过程中应注意的问题。 相似文献
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企业应用SPC的几个误区 总被引：1，自引：0，他引：1
作为一种提高产品质量和保证能力的重要技术措施，统计过程控制技术（SPC）在国内外各类生产厂家得到了普遍重视，本文针对SPC统计过程控制技术在企业应用时经常遇到的一些问题进行探讨，旨在推进SPC的贯彻和实施。 相似文献
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《International Journal of Forecasting》2021,37(4):1509-1519
The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based. 相似文献
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《International Journal of Forecasting》2019,35(2):733-740
We propose new models for analyzing pairwise comparison data, such as that relating to sports. We focus on changes in players’ strengths and the prediction of future results. Our models are based on the Thurstone-Mosteller and Bradley–Terry models, and make use of the time variation in the parameters. Furthermore, we apply our models to data from the Japanese traditional sport sumo, and analyze this data. The proposed models perform better than the standard Thurstone-Mosteller and Bradley–Terry models according to both the Akaike information criterion and the Brier score. We compare the proposed models in detail by focusing on individual sumo wrestlers. 相似文献
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文章针对传统的公路施工网络图优化中存在的问题和不足,运用启发式搜索算法和遗传算法,对公路施工网络优化从时间、费用、资源三方面进行网络计划的优化和调整。 相似文献
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Chung-Ho Chen 《Quality and Quantity》2009,43(4):653-659
In 2001, Huang presented the problem of determining the optimum process mean and standard deviation based on considering the trade-off relationship between the process adjustment cost and the quality loss of product. They considered the normal quality characteristic and adopted the quadratic quality loss function for measuring the product quality. In this paper, we further propose the problem of process optimization and reconsider the modified Huang’s model under the specified process capability index value for determining the optimum process parameters. The symmetric quadratic, asymmetric quadratic, and asymmetric linear quality loss functions will be adopted for evaluating the product quality. Finally, the numerical example and the sensitivity analysis of parameters will be provided for illustration. 相似文献
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《International Journal of Forecasting》2023,39(3):1185-1204
This paper proposes LASSO estimation specific for panel vector autoregressive (PVAR) models. The penalty term allows for shrinkage for different lags, for shrinkage towards homogeneous coefficients across panel units, for penalization of lags of variables belonging to another cross-sectional unit, and for varying penalization across equations. The penalty parameters therefore build on time series and cross-sectional properties that are commonly found in PVAR models. Simulation results point towards advantages of using the proposed LASSO for PVAR models over ordinary least squares in terms of forecast accuracy. An empirical forecasting application including 20 countries supports these findings. 相似文献
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This paper shows how to compute the standard errors for partial effects of exogenous firm characteristics influencing firm
inefficiency under a range of popular stochastic frontier model specifications. We also develop an R2-type measure to summarize the overall explanatory power of the exogenous factors on firm inefficiency. The paper also applies
a recently developed model selection procedure to choose among alternative stochastic frontier specifications using data from
household maize production in Kenya. The magnitude of estimated partial effects of exogenous household characteristics on
inefficiency turns out to be very sensitive to model specification, and the model selection procedure leads to an unambiguous
choice of best model. We propose a bootstrapping procedure to evaluate the size and power of the model selection procedure.
The empirical application also provides further evidence on how household characteristics influence technical inefficiency
in maize production in developing countries.
相似文献
| Yanyan LiuEmail: |
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《International Journal of Forecasting》2014,30(4):996-1015
In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information from a large number of predictors. So far, the dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities for forecasting twenty important macroeconomic variables. These alternative models can handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show, both analytically and empirically, that combing forecasts from LASSO-based models with those from dynamic factor models can reduce the mean square forecast error (MSFE) further. Our three main findings can be summarized as follows. First, for most of the variables under investigation, all of the LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at economically meaningful block levels, the new methods greatly enhance the interpretability of the models. Third, once forecasts from a LASSO-based approach are combined with those from a dynamic factor model by forecast combination techniques, the combined forecasts are significantly better than either dynamic factor model forecasts or the naïve random walk benchmark. 相似文献
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本案例研究以客户终生价值测度模型为基本工具,通过挖掘客户历史购买信息,在预测客户未来购买情况的基础上对客户终生价值进行测度,探索其对企业实践的管理意义.以大连友好商城为案例,针对其当前存在的问题,探讨并选择合适的客户终生价值测度模型;通过Pareto/NBD模型预测客户未来的购买次数,Gamma-Gamma模型预测未来的平均购买金额,在此基础上计算客户终生价值;对商城现有客户进行分类,深入分析了不同类别客户的购买特征,提出相应的差异化营销策略.研究表明,通过测度客户终生价值,可以帮助企业识别并分类客户,进而实施差异化的营销策略,提升企业竞争优势. 相似文献
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《International Journal of Forecasting》2020,36(2):515-530
We develop a method for forecasting the distribution of the daily surface wind speed at timescales from 15-days to 3-months in France. On such long-term timescales, ensemble predictions of the surface wind speed have poor performance, however, the wind speed distribution may be related to the large-scale circulation of the atmosphere, for which the ensemble forecasts have better skill. The information from the large-scale circulation, represented by the 500 hPa geopotential height, is summarized into a single index by first running a PCA and then a polynomial regression. We estimate, over 20 years of daily data, the conditional probability density of the wind speed at a specific location given the index. We then use the ECMWF seasonal forecast ensemble to predict the index for horizons from 15-days to 3-months. These predictions are plugged into the conditional density to obtain a distributional forecast of surface wind. These probabilistic forecasts remain sharper than the climatology up to 1-month forecast horizon. Using a statistical postprocessing method to recalibrate the ensemble leads to further improvement of our probabilistic forecast, which then remains calibrated and sharper than the climatology up to 3-months horizon, particularly in the north of France in winter and fall. 相似文献
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The present paper tests a new model comparison methodology by comparing multiple calibrations of three agent-based models of financial markets on the daily returns of 24 stock market indices and exchange rate series. The models chosen for this empirical application are the herding model of Gilli and Winker (2003), its asymmetric version by Alfarano et al. (2005) and the more recent model by Franke and Westerhoff (2011), which all share a common lineage to the herding model introduced by Kirman (1993). In addition, standard ARCH processes are included for each financial series to provide a benchmark for the explanatory power of the models. The methodology provides a consistent and statistically significant ranking of the three models. More importantly, it also reveals that the best performing model, Franke and Westerhoff, is generally not distinguishable from an ARCH-type process, suggesting their explanatory power on the data is similar. 相似文献
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Gabriel Perez-Quiros 《International Journal of Forecasting》2018,34(1):139-141
The excellent article by David Hendry describes how to nest “theory-driven” and “data-driven” approaches when deciding between alternative models in macroeconomics. The article’s final conclusion is that theory allows the econometrician to select a set of variables, while data allows him/her to select across a wide range of alternatives: lag selection, structural breaks, functional forms, etc. The aim of this discussion is to provide the reader with an illustration of this proposed mixing of theory and data in one of the fields mentioned in the paper, macroeconomic forecasting. 相似文献
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《Socio》2023
Machine learning models are boosting Artificial Intelligence applications in many domains, such as automotive, finance and health care. This is mainly due to their advantage, in terms of predictive accuracy, with respect to classic statistical models. However, machine learning models are much less explainable: less transparent, less interpretable. This paper proposes to improve machine learning models, by proposing a model selection methodology, based on Lorenz Zonoids, which allows to compare them in terms of predictive accuracy significant gains, leading to a selected model which maintains accuracy while improving explainability. We illustrate our proposal by means of simulated datasets and of a real credit scoring problem. The analysis of the former shows that the proposal improves alternative methods, based on the AUROC. The analysis of the latter shows that the proposal leads to models made up of two/three relevant variables that measure the profitability and the financial leverage of the companies asking for credit. 相似文献