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151.
激烈的市场竞争使得客户资源争夺成为企业成功的关键因素.正确的客户分类能帮助企业有效分配营销资源,针对性地加强客户联系、改善客户关系、增强客户忠诚度,使得企业获得核心竞争力.基于此,本文分析了客户分类与客户关系管理的关系以及神经网络技术在客户分类中的应用情况,利用自组织神经网络研究客户分类问题,以RFM为分析变量建立客户分类模型;比较输出层构造分别为“2×2”、“3×3”和“4×4”的自组织神经网络模型分类结果,确定最佳的自组织神经网络模型;利用层级分析法对“4×4”型自组织神经网络模型划分的16类客户进行价值分析、价值排序,得到每类客户具体的相对价值大小,为企业准确认识客户价值、合理分配资源提供依据. 相似文献
152.
An artificial neural forecasting model is developed for air transport passenger analysis. It uses a preprocessing method that decomposes information to reveal relevant features from the data. It is found that neural processing outperforms the traditional econometric approach and offers generalization on time series behavior, even where there are only small samples. 相似文献
153.
The modeling process of bubbles, using advanced mathematical and econometric techniques, is a young field of research. In this context, significant model misspecification could result from ignoring potential non-linearities. More precisely, the present paper attempts to detect and date non-linear bubble episodes. To do so, we use Neural Networks to capture the neglected non-linearities. Also, we provide a recursive dating procedure for bubble episodes. When using data on stock price-dividend ratio S&P500 (1871.1–2014.6), employing Bayesian techniques, the proposed approach identifies more episodes than other bubble tests in the literature, while the common episodes are, in general, found to have a longer duration, which is evidence of an early warning mechanism (EWM) that could have important policy implications. 相似文献
154.
《International Journal of Forecasting》2022,38(4):1448-1459
In this study, we addressed the problem of point and probabilistic forecasting by describing a blending methodology for machine learning models from the gradient boosted trees and neural networks families. These principles were successfully applied in the recent M5 Competition in both the Accuracy and Uncertainty tracks. The key points of our methodology are: (a) transforming the task into regression on sales for a single day; (b) information-rich feature engineering; (c) creating a diverse set of state-of-the-art machine learning models; and (d) carefully constructing validation sets for model tuning. We show that the diversity of the machine learning models and careful selection of validation examples are most important for the effectiveness of our approach. Forecasting data have an inherent hierarchical structure (12 levels) but none of our proposed solutions exploited the hierarchical scheme. Using the proposed methodology, we ranked within the gold medal range in the Accuracy track and within the prizes in the Uncertainty track. Inference code with pre-trained models are available on GitHub.1 相似文献
155.
在阅读大量国内外文献的基础上,对原始性创新人格影响因素进行了总结和补充,通过建立和训练BP神经网络确定了影响因素综合权值,结合DEMATEL算法,对1901-2012年诺贝尔物理学奖获得者原始性创新人格形成影响因素进行了重要性分析。按照中心度计算结果,对原始性创新人格形成影响因素进行了排序,根据原因度结果,判定哪些属于原因类影响因素,探寻诺贝尔物理学奖获得者原始性创新人格形成规律。最后,提出了有益于塑造原始性创新人格的对策。 相似文献
156.
Robert FildesAuthor Vitae Nikolaos KourentzesAuthor Vitae 《International Journal of Forecasting》2011,27(4):968
Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority. 相似文献
157.
Jörg D. Wichard 《International Journal of Forecasting》2011,27(3):700
We propose a simple way of predicting time series with recurring seasonal periods. Missing values of the time series are estimated and interpolated in a preprocessing step. We combine several forecasting methods by taking the weighted mean of forecasts that were generated with time-domain models which were validated on left-out parts of the time series. The hybrid model is a combination of a neural network ensemble, an ensemble of nearest trajectory models and a model for the 7-day cycle. We apply this approach to the NN5 time series competition data set. 相似文献
158.
ARIMA融合神经网络的人民币汇率预测模型研究 总被引:1,自引:0,他引:1
熊志斌 《数量经济技术经济研究》2011,(6)
本文在深入分析了单整自回归移动平均(ARIMA)模型与神经网络(NN)模型特点的基础上,建立了ARIMA融合NN的人民币汇率时间序列预测模型。其基本思想是充分发挥两种模型在线性空间和非线性空间的预测优势,即将汇率时间序列的数据结构分解为线性自相关主体和非线性残差两部分,首先用ARI-MA模型预测序列的线性主体,然后用NN模型对其非线性残差进行估计,最终合成为整个序列的预测结果。通过对三种人民币汇率序列的仿真实验表明,融合模型的预测准确率显著高于包括随机游走模型在内的单一模型的预测准确率,从而证实了融合模型用于汇率预测的有效性。这一结果也表明,人民币汇率市场并不符合有效市场假设,可以通过模型对汇率未来走势做出较准确预测。 相似文献
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