具有新初始值的Holt-Winters乘法方法的启发式方法

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI:10.3233/mas-221415

Victor Anthonysamy, Khadar Babu, C. Chesneau

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引用次数: 0

摘要

当检测到趋势性和季节性时，Holt-Winters乘法方法是预测时间序列数据最常用的方法之一。为水平、趋势和季节性选择合适的初始值在这种方法中起着至关重要的作用。本文提出了一种新的、有效的Holt-Winters乘性方法初值选择方法。共使用12种类型的农业卫星反向散射值进行分析、估计，并与现有的Hansun和Holt-Winters方法以及所提出的具有最佳平滑常数的初始设置方法进行比较。根据对平均绝对百分比误差、对称平均绝对百分比错误、Theil-U统计量和均方根误差的分析，在本实验中，所提出的方法优于现有方法。

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Heuristics approach for the Holt-Winters multiplicative method with new initial values

When trend and seasonality are detected, the Holt-Winters multiplicative approach is one of the most commonly used methods for forecasting time series data. Choosing the proper initial values for level, trend, and seasonality plays a vital role in this method. In this paper, a new and efficient procedure to choose the initial values for the Holt-Winters multiplicative method is developed. A total of 12 types of agricultural satellite backscatter values are used for analysis, estimated, and compared with the existing Hansun and Holt-Winters methods and the proposed initial setting method with the best smoothing constants. According to the analysis of the mean absolute percentage error, symmetric mean absolute percentage error, Theil-U statistics, and root mean squared error, the proposed approaches outperformed the existing methods in this experiment.

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来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.