When to choose the simple average in forecast combination

Blanc Sebastian M.; Setzer Thomas

首页> 外文期刊>Journal of Business Research >When to choose the simple average in forecast combination

【24h】

When to choose the simple average in forecast combination

机译：何时选择预测组合中的简单平均值

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Numerous forecast combination techniques have been proposed. However, these do not systematically outperform a simple average (SA) of forecasts in empirical studies. Although it is known that this is due to instability of learned weights, managers still have little guidance on how to solve this "forecast combination puzzle", i.e., which combination method to choose in specific settings. We introduce a model determining the yet unknown asymptotic out-of-sample error variance of the two basic combination techniques: SA, where no weightings are learned, and so-called optimal weights that minimize the in-sample error variance. Using the model, we derive multi-criteria boundaries (considering training sample size and changes of the parameters which are estimated for optimal weights) to decide when to choose SA. We present an empirical evaluation which illustrates how the decision rules can be applied in practice. We find that using the decision rules is superior to all other considered combination strategies. (C) 2016 Elsevier Inc. All rights reserved.

机译：已经提出了许多预测组合技术。但是，在经验研究中，这些并不能系统地胜过简单的预测平均值（SA）。尽管已知这是由于学习的权重的不稳定性造成的，但是管理人员仍然对如何解决这个“预测组合难题”，即在特定设置中选择哪种组合方法的指导很少。我们介绍一种模型，该模型确定两种基本组合技术的未知的渐进样本外误差方差：SA，其中不学习加权，以及使样本内误差方差最小的所谓最佳权重。使用该模型，我们得出多准则边界（考虑培训样本大小和为最佳权重而估计的参数变化），以决定何时选择SA。我们提出了一项实证评估，它说明了如何在实践中应用决策规则。我们发现使用决策规则优于所有其他考虑的组合策略。（C）2016 Elsevier Inc.保留所有权利。

著录项

来源
《Journal of Business Research》 |2016年第10期|3951-3962|共12页
作者
Blanc Sebastian M.; Setzer Thomas;
展开▼
作者单位

Karlsruhe Inst Technol, Fritz Erler Str 23, D-76131 Karlsruhe, Germany;

Karlsruhe Inst Technol, Fritz Erler Str 23, D-76131 Karlsruhe, Germany;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词
Forecast combination; Simple average; Optimal weights; Structural changes;

机译：预测组合;简单平均;最优权重;结构变化;

相似文献

外文文献
中文文献
专利

1. A robust combination approach for short-term wind speed forecasting and analysis - Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model [J] . Wang Jianzhou, Hu Jianming Energy . 2015,第DECaPTa1期

机译：一种用于短期风速预测和分析的强大组合方法-使用GPR（ARIMA（自回归综合移动平均值），ELM（极限学习机），SVM（支持向量机）和LSSVM（最小二乘SVM）预测进行组合高斯过程回归模型
2. Choosing Among competing Econometric Forecasts: Regression-based Forecast Combination Using Model Selection [J] . Norman R. Swanson, Tian Zeng Journal of Forecasting . 2001,第6期

机译：在相互竞争的计量经济学预测中进行选择：使用模型选择的基于回归的预测组合
3. Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and na?ve forecasting methods [J] . Christopher J. Lynch, Ross Gore Data in Brief . 2021,第3期

机译：在Covid-19流行性数据集早期应用中的一个，三个和七天预报的应用使用搬家平均，自回归，自回归移动平均，自回归综合移动平均线和NA ve预测方法
4. Forecasting System of Office Supplies Demand Using Simple Moving Average and Simple Exponential Smoothing (Case Study: Regional Office of The Ministry of Religious Affairs of Jakarta) [C] . Elvi Fetrina, Meinarini Catur Utami, Anita Permatasari International Conference on Science and Technology . 2018

机译：简单移动平均值和简单指数平滑的办公用品需求预测系统（凯塔塔宗教事务部区域办事处）
5. The use of the moving average forecasting, linear trend forecasting, and exponential smoothing forecasting techniques in education. [D] . Ilsley, Peter Spencer. 2002

机译：在教育中使用移动平均线预测，线性趋势预测和指数平滑预测技术。
6. Application of one- three- and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average autoregressive autoregressive moving average autoregressive integrated moving average and naïve forecasting methods [O] . Christopher J. Lynch, Ross Gore 2021

机译：在Covid-19流行数据集早期应用中的一个三个和七天预报的应用使用移动平均自回归自回归移动平均自回归综合移动平均值和天真预测方法
7. 2001), “Choosing among competing econometric forecasts: regression-based forecast combination using model selection [O] . Norman R. Swanson, Tian Zeng 2015

机译：2001年，“在竞争的计量经济预测中进行选择：使用模型选择的基于回归的预测组合

When to choose the simple average in forecast combination

摘要

著录项

相似文献

相关主题

期刊订阅