检索结果-内蒙古大学图书馆

Conference on Intelligent Information Systems (IIS 97)

作者： Chiraphadhanakul, S Dangprasert, P Avatchanakorn, V Assumption Univ Grad Sch Comp Informat Syst Bangkok Thailand

ISBN: (纸本)0818682183

In this paper, a new forecasting algorithm using features of generic algorithms (GAs) is proposed, The algorithms combines the powerful search of GAs and their capability to learn patterns relationship, of pas;data in ol der to forecast future values. The genetic forecasting algorithm consists of two steps:forecasting and learning steps. GAs are used in the forecasting step to estimate parameters of the problem domain. Tile patterns learning is taken into account in the algorithm to capture the patterns relationship of learning data. The results of the algorithm are examined by applying them in two financial problem domains: commercial banks deposit and bankruptcy prediction.

关键词： genetic algorithms forecasting algorithm pattern learning financial applications

来源：评论

学校读者我要写书评

暂无评论

A NEURAL-NETWORK-BASED forecasting algorithm FOR RETAIL INDUSTRY

A NEURAL-NETWORK-BASED FORECASTING ALGORITHM FOR RETAIL INDU...

引用

International Conference on Machine Learning and Cybernetics

作者： Gao, Yue-Fang Liang, Yong-Sheng Liu, Ying Zhan, Shao-Bin Ou, Zhi-Wei Shenzhen Inst Informat Technol Shenzhen 518029 Peoples R China Wal Mart Global Procurement Origin Replenishment Shenzhen 518001 Peoples R China

ISBN: (纸本)9781424447053

To obtain the inherent laws from large amounts of data records in retail industry and to provide valuable information for retailers, this paper presents a neural-network-based forecasting algorithm, which adopts Holt-Winters' model and a neural network. Different from traditional forecasting algorithms, this algorithm rearranges Holt-Winters model, and builds a neural network on it. Furthermore, it puts forward a training algorithm to optimize the adjustable neural network weights by minimizing a defined cost function, which has greatly improved the forecasting accuracy. Experimental results at the end of this paper also prove the superiorities.

关键词： Holt-Winters' model Neural network Retail industry forecasting algorithm

来源：评论

学校读者我要写书评

暂无评论

Realization of a Demand forecasting algorithm for Retail Industry

Realization of a Demand Forecasting Algorithm for Retail Ind...

引用

23rd Chinese Control and Decision Conference

作者： Gao, Yuefang Liang, Yongsheng Zhan, Shaobin Ren, Xianyi Ou, Zhiwei Shenzhen Inst Informat Technol Dept Software Engn Shenzhen 518029 Peoples R China Shenzhen Inst Informat Technol Shenzhen 518029 Peoples R China Wal Mart Global Procurement Dept Origin Replenishment Shenzhen 518001 Peoples R China

ISBN: (纸本)9781424487363

This paper proposes the realization of a demand forecasting algorithm for retail industry. It categorizes the merchandises into four cases according to the availability of the historical demand, and suggests processes to deal with each individual case. This paper adopts Holt-Winters' model for the forecasting algorithm, and provides method to initialize the parameters. Due to the dependency of Holt-Winters' method on the reliability of historical demand collection, the accuracy of the proposed algorithm is limited to a certain degree. However, this applicable process adopts smoothing methods to minimize the influence by unreliable historical demand and could provide reference for the relevant research.

关键词： Retail Industry forecasting algorithm Realization Holt-Winters' model

来源：评论

学校读者我要写书评

暂无评论

A Frequency Decomposition-Based Hybrid forecasting algorithm for Short-Term Reactive Power

引用

ENERGIES 2021年第20期14卷 6606页

作者： Du, Jiabao Yue, Changxi Shi, Ying Yu, Jicheng Sun, Fan Xie, Changjun Su, Tao Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China China Elect Power Res Inst Wuhan 430070 Peoples R China Xinjiang Elect Power Res Inst State Gird Urumqi 830000 Peoples R China

This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reactive power. This decomposition process can compress the high frequency of reactive power and benefits the following separate forecasting. Long short-term memory is proposed for the high-frequency feature of reactive power to deal with the forecasting difficulty caused by strong signal disturbance and randomness. In contrast, random forest regression is applied to the low-frequency part in order to speed up the forecasting. Four classical algorithms and four hybrid algorithms based on different signal decompositions are compared with the proposed algorithm, and the results show that the proposed algorithm outperforms those algorithms. The predicting index RMSE decreases to 0.687, while the fitting degree R-2 gradually approaches 1 with a step-by-step superposition of high-frequency signals, indicating that the proposed decomposition-predicting reconstruction strategy is effective.

关键词： reactive power forecasting algorithm ensemble empirical mode decomposition long short-term memory random forest regression

来源：评论

学校读者我要写书评

暂无评论

Research on Material Demand forecasting algorithm Based on Multi-Dimensional Feature Fusion

引用

INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2023年第1期14卷 1页

作者： She, Shi-Yao Yuan, Fang-Fang Li, Jun-Ke Dai, Hong-Wei China Shipbuilding Ind Corp Purificat Equipment Res Inst Beijing Peoples R China Dongfang Elect Co Ltd Harbin Peoples R China

Material demand forecasting has a profound impact on the supply chain and is an important prerequisite for manufacturing enterprises to produce. In order to accurately predict the material demand of enterprises, this paper proposes a material demand forecasting algorithm based on multi-dimensional feature fusion (DFMF). Secondly, in order to obtain the spatial features, the vector representation of the relevant materials of a material is obtained through the attention mechanism. Then, the authors aggregate the relevant material representation and material vector representation of materials to obtain the final material vector representation through aggregation function. Then the final material vector representation under different time scales is used as input, and the prediction value of material demand is obtained by using BP neural network. Finally, experiments show that the model can effectively obtain multi-dimensional features of materials for prediction, and the prediction results have high accuracy.

关键词： Cycle Features forecasting algorithm Historical Information Multi-Dimensional Feature Fusion Neural Network

来源：评论

学校读者我要写书评

暂无评论

Research on a Neural-network-based forecasting algorithm for Retail Industry

Research on a Neural-network-based Forecasting Algorithm for...

引用

8th World Congress on Intelligent Control and Automation (WCICA)

作者： Gao, Yue-Fang Liang, Yong-Sheng Tang, Fei Ou, Zhi-Wei Peng, Yun-Jian Liang, Jin Shenzhen Inst Informat Technol Dept Software Engn Shenzhen Peoples R China Shenzhen Inst Informat Technol Dept Sci Res & Equipment Shenzhen Peoples R China Wal Mart Global Procurement Dept Origin Replenishment Shenzhen Peoples R China South China Univ Technol Coll Automat Guangzhou Peoples R China

ISBN: (纸本)9781424467129

关键词： Holt-Winters' model neural network retail industry forecasting algorithm

来源：评论

学校读者我要写书评

暂无评论

Realization of a Demand forecasting algorithm for Retail Industry

Realization of a Demand Forecasting Algorithm for Retail Ind...

引用

2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Yuefang Gao Xianyi Ren Shaobin Zhan Yongsheng Liang Zhiwei Ou Department of Software Engineering Shenzhen Institute of Information TechnologyShenzhen518029 Shenzhen Institute of Information Technology Shenzhen518029 Department of Origin Replenishment Wal-Mart Global ProcurementShenzhen518001

This paper proposes the realization of a demand forecasting algorithm for retail industry. It categorizes the merchandises into four cases according to the availability of the historical demand, and suggests processes to deal with each individual case. This paper adopts Holt-Winters’ model for the forecasting algorithm, and provides method to initialize the parameters. Due to the dependency of Holt-Winters’ method on the reliability of historical demand collection, the accuracy of the proposed algorithm is limited to a certain degree. However, this applicable process adopts smoothing methods to minimize the influence by unreliable historical demand and could provide reference for the relevant research.

关键词： Retail Industry forecasting algorithm Realization Holt-Winters'model

来源：评论

学校读者我要写书评

暂无评论

A NEURAL-NETWORK-BASED forecasting algorithm FOR RETAIL INDUSTRY

A NEURAL-NETWORK-BASED FORECASTING ALGORITHM FOR RETAIL INDU...

引用

2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： YUE-FANG GAO YONG-SHENG LIANG SHAO-BIN ZHAN YING LIU ZHI-WEI OU Shenzhen institute of information technology Shenzhen 518029 China Origin Replenishment Wal-Mart Global Procurement Shenzhen 518001 China

To obtain the inherent laws from large amounts of data records in retail industry and to provide valuable information for retailers, this paper presents a neural-network-based forecasting algorithm, which adopts Holt-Winters' model and a neural network. Different from traditional forecasting algorithms, this algorithm rearranges Holt-Winters model, and builds a neural network on it Furthermore, it puts forward a training algorithm to optimize the adjustable neural network weights by minimizing a defined cost function, which has greatly improved the forecasting accuracy. Experimental results at the end of this paper also prove the superiorities.

关键词： Holt-Winters' model Neural network Retail industry forecasting algorithm

来源：评论

学校读者我要写书评

暂无评论

A forecasting algorithm for Latency Compensation in Indirect Human-Computer Interactions

A Forecasting Algorithm for Latency Compensation in Indirect...

引用

European Control Conference

作者： Rosane Ushirobira Denis Efimov Géry Casiez Nicolas Roussel Wilfrid Perruquetti Inria Non-A team 40 av. Halley 59650 Villeneuve d'Ascq France Inria Mjolnir team 40 av. Halley 59650 Villeneuve d'Ascq France

ISBN: (纸本)9781509025923

Human-computer interactions are greatly affected by the latency between the human input and the system visual response. The compensation of this latency is an important problem for the HCI (human-computer interaction) community. In this work, a simple forecasting algorithm is developed for latency compensation in indirect interaction using a mouse, based on numerical differentiation. Several differentiators are compared, including a novel algebraic version. An optimized procedure is developed for tuning the parameters of the algorithm. The efficiency is demonstrated on real data, measured with a 1 ms sampling time.

关键词： forecasting algorithm Latency Compensation Indirect Human-Computer Interactions

来源：评论

学校读者我要写书评

暂无评论

Tuning forecasting algorithms for black swans

引用

IFAC-PapersOnLine 2019年第13期52卷 1496-1501页

作者： Prestwich, S.D. Department of Computer Science University College Cork Ireland

forecasting algorithms based on exponential smoothing have smoothing factors, and it is often recommended that these be tuned to minimise an error measure on observed data. We show that forecasting algorithms such as simple exponential smoothing and Croston's method cannot always be optimally tuned to time series using any of several error measures. We propose a data augmentation approach: adding hypothetical non-stationary time series (which we call "black swans" as they represent unseen pathological cases) to the training data, and minimising a weighted error. The choice of black swans is a form of judgemental forecasting that requires experts to explicitly state their assumptions on unseen data. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： forecasting Errors Time series Time series analysis Black swan Exponential smoothing forecasting algorithm Non stationarities Non stationary time series Optimisations Simple exponential smoothing Smoothing factors

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：