中国科学院机构知识库网格系统: 检索

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	An extreme bias-penalized forecast combination approach to commodity price forecasting 期刊论文 OAI收割 INFORMATION SCIENCES, 2022, 卷号: 615, 页码: 774-793 作者: Zhang, Yifei; Wang, Jue; Yu, Lean; Wang, Shouyang \| 收藏 \| 浏览/下载：30/0 \| 提交时间：2023/02/07 Forecast combination Elastic net Extreme bias Weight-sparsity Artificial bee colony algorithm
	Artificial bee colony-based combination approach to forecasting agricultural commodity prices 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF FORECASTING, 2022, 卷号: 38, 期号: 1, 页码: 21-34 作者: Wang, Jue; Wang, Zhen; Li, Xiang; Zhou, Hao \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2022/04/02 Agricultural commodity price Forecast combination Semi-heterogeneous combination Artificial bee colony algorithm Denoising technique
	Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach 期刊论文 OAI收割 JOURNAL OF TRAVEL RESEARCH, 2021, 页码: 19 作者: Sun, Yuying; Zhang, Jian; Li, Xin; Wang, Shouyang \| 收藏 \| 浏览/下载：38/0 \| 提交时间：2022/04/02 forecast combination nonparametric estimation structural changes tourism demand time-varying jackknife model averaging
	A New Two-Stage Approach with Boosting and Model Averaging for Interval-Valued Crude Oil Prices Forecasting in Uncertainty Environments 期刊论文 OAI收割 FRONTIERS IN ENERGY RESEARCH, 2021, 卷号: 9, 页码: 11 作者: Huang, Bai; Sun, Yuying; Wang, Shouyang \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2022/04/02 crude oil prices forecasting forecast combination interval-valued time series model averaging vector L2-boosting
	Time-varying model averaging? 期刊论文 OAI收割 JOURNAL OF ECONOMETRICS, 2021, 卷号: 222, 期号: 2, 页码: 974-992 作者: Sun, Yuying; Hong, Yongmiao; Lee, Tae-Hwy; Wang, Shouyang; Zhang, Xinyu \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2021/06/01 Asymptotic optimality Forecast combination Local stationarity Model averaging Structural change Time-varying model averaging
	A multi-granularity heterogeneous combination approach to crude oil price forecasting 期刊论文 OAI收割 ENERGY ECONOMICS, 2020, 卷号: 91, 页码: 9 作者: Wang, Jue; Zhou, Hao; Hong, Tao; Li, Xiang; Wang, Shouyang \| 收藏 \| 浏览/下载：39/0 \| 提交时间：2021/01/14 Crude oil Price Forecast combination Feature selection Multi-granularity Artificial bee colony
	A semi-heterogeneous approach to combining crude oil price forecasts 期刊论文 OAI收割 INFORMATION SCIENCES, 2018, 卷号: 460, 页码: 279-292 作者: Wang, Jue; Li, Xiang; Hong, Tao; Wang, Shouyang \| 收藏 \| 浏览/下载：44/0 \| 提交时间：2018/10/07 Crude oil price Decomposition and reconstruction Forecast combination Model selection
	Forecasting with model selection or model averaging: a case study for monthly container port throughput 期刊论文 OAI收割 TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2016, 卷号: 12, 期号: 4, 页码: 366-384 作者: Gao, Yan; Luo, Meifeng; Zou, Guohua \| 收藏 \| 浏览/下载：31/0 \| 提交时间：2018/07/30 Model combination model selection time series forecast structural change VAR model container throughput
	Study on color model conversion for camera with neural network based on the combination between second general revolving combination design and genetic algorithm (EI CONFERENCE) 会议论文 OAI收割 ICO20: Illumination, Radiation, and Color Technologies, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Z. 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/03/25 Munsell color system is selected to establish the mutual conversion between RGB and Lab* color model for camera. The color luminance meter and CCD camera synchronously measure the same color card XYZ value is gotten from the color luminance meter the training error is 0.000748566 it can show that the method combining second general revolving combination design with genetic algorithm can optimize the hidden-layer structure of neural network. Using the data of testing set to test this network and calculating the color difference between forecast value and true value the color picture captured from CCD camera is expressed for RGB value as the input of neural network and the Lab* value converted from XYZ value is regarded as the real color value of target card which the difference is not obvious comparing with forecast result the maximum is 5.6357 NBS namely the output of neural network. The neural network of two hidden-layers is considered the minimum is 0.5311 NBS so the second general revolving combination design is introduced into optimizing the structure of neural network and the average of color difference is 3.1744 NBS. which can carry optimization through unifying project design data processing and the precision of regression equation. Their mathematics model of encoding space is gained and the significance inspection shows the confidence degree of regression equation is 99%. The mathematics model is optimized by genetic algorithm optimization solution is gotten and function value of the goal is 0.0007168. The neural network of the optimization solution is trained