Greymodels:R 中灰色预测模型的闪亮软件包

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-05-03 DOI:10.1007/s10614-024-10610-8
Havisha Jahajeeah, Aslam A. E. F. Saib
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引用次数: 0

摘要

Greymodels 软件包为使用灰色模型对不完整或小型数据集进行统计建模和预测提供了一个 R 语言交互界面。该软件包基于 Shiny 框架,设计用于处理具有不同属性和特征的单变量和多变量数据集。该软件包的功能通过几个示例进行了演示,尤其是用户友好界面的展示,让用户可以轻松比较不同预测模型的性能,并在用户选择的置信区间内可视化预测值的图形图表。Greymodels软件包中的内置算法是GM/((1,\,1)\)模型的扩展或混合,本文概述了基本灰色模型的理论背景,我们还提出了该软件包中的PSO-GM/((1,\,1)\)算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Greymodels: A Shiny Package for Grey Forecasting Models in R

The Greymodels package presents an interactive interface in R for the statistical modelling and forecasting of incomplete or small datasets using grey models. The package, based on the Shiny framework, has been designed to work with univariate and multivariate datasets having different properties and characteristics. The functionality of the package is demonstrated with a few examples and in particular, the user-friendly interface is shown to allow users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within a user chosen confidence interval. The built-in algorithms in the Greymodels package are extensions or hybrids of the GM\((1,\,1)\) model, and this article covers an overview of the theoretical background of the basic grey model and we also propose a PSO-GM\((1,\,1)\) algorithm in this package.

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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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