相关实验视频
适应性需求预测框架,在生命周期变异性沿着重量集的回归和机器学习模型
Islam M Hammam1, Amin K El-Kharbotly2, Yomna M Sadek2
1Design and Production Engineering Department, Ain Shams University, Cairo, Egypt. islam.maged@eng.asu.edu.eg.
Scientific reports
|November 4, 2025
概括
本研究介绍了一种混合预测框架,将ARIMA和XGBoost模型结合起来,用于准确的需求预测. 适应性方法提高了预测准确度,特别是对于复杂的,不断变化的产品需求模式.
科学领域:
- 业务分析 业务分析
- 机器学习 机器学习
- 运营研究 运营研究
背景情况:
- 准确的需求预测对于在具有较短产品生命周期的动态市场中的商业决策至关重要.
- 像ARIMA这样的传统方法在线性趋势方面出色,而机器学习 (XGBoost) 则处理复杂的非线性模式.
研究的目的:
- 开发一个适应性,混合预测框架,在各种产品生命周期阶段准确预测需求.
- 专门解决产品下降阶段预测需求的挑战.
主要方法:
- 一个适应性,混合框架,集成ARIMA和XGBoost模型,使用加权合奏策略.
- 动态模型选择:ARIMA用于线性趋势,结合XGBoost用于通过诊断分析检测到的非线性.
- 网格搜索优化,通过最大限度地减少根平均平方误差 (RMSE) 来确定最佳集合重量.
主要成果:
- 混合框架在大多数数据集上实现了低于13%的平均绝对百分比误差 (MAPE).
- 与传统的ARIMA模型相比,它在高变量的需求模式方面表现出高达80%的改进.
- 整体模型显示出更高的灵活性和准确性,特别是对于结合线性和非线性组件的需求模式.
结论:
- 混合建模为提高需求预测准确性和运营规划提供了实际优势.
- 该框架有效地平衡了ARIMA的可解释性和时间序列能力与XGBoost的非线性建模能力.
- 这种方法在需要强有力的需求预测的各种行业中是有益的.
相关概念视频
01:18Steps in Outbreak Investigation
481
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
481
01:25Multiple Regression
3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
237
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
237
01:03Prediction Intervals
3.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.2K
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
278
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
278
01:11Regression Analysis
7.9K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.9K