Time series decomposition

The research conducted using this univariate data set is on time series decomposition and a review of how to implement four decomposition methods namely: Classical decomposition, X11, Signal extraction in ARIMA time series(SEATS) and Seasonal trend decomposition procedure based on Loess(STL) decomposition. Following decomposition, forecasting with decomposition is implemented on the monthly electricity available for distribution to South Africa by Eskom time series data set. R Studio was used for the research. explain the components of a time series, moving averages, . <br>Other data sets as well as those that are R built-in were used in the second section of the work, that is, to illustrate the components of a time series and moving averages. Following this the monthly electricity available for distribution to South Africa by Eskom time series data set was used for the third and fourth section of the research. That is, to implement the time series decomposition methods, analyze the random component of the methods, as well as to forecast with decomposition and to compute the forecast accuracy of four different forecasting methods.本研究采用该单变量数据集（univariate data set）开展时间序列分解（time series decomposition）相关研究，并综述四种分解方法的实现路径，分别为经典分解（Classical decomposition）、X11分解法、ARIMA时序信号提取法（Signal extraction in ARIMA time series，SEATS）以及基于Loess的季节趋势分解程序（Seasonal trend decomposition procedure based on Loess，STL）。完成分解后，针对南非国家电力公司（Eskom）的月度可供电时序数据集，开展基于分解的预测实验。本研究全程依托R Studio完成，并将阐释时间序列分量与移动平均的相关概念。 本研究第二部分采用其他公开数据集及R语言内置数据集，用于演示时间序列分量与移动平均的原理。在此之后，第三、第四部分研究均采用前述南非Eskom月度可供电时序数据集，具体研究内容包括：实现上述四种时间序列分解方法，分析各方法的随机分量，开展基于分解的预测实验，并计算四种不同预测方法的预测精度。

搜集汇总

背景与挑战

背景概述

该数据集专注于时间序列分解研究，详细介绍了经典分解、X11、SEATS和STL四种分解方法，并以南非Eskom公司每月电力分配数据为实际案例，进行分解、随机成分分析及预测准确性评估。数据集支持使用R Studio进行方法实现和预测分析，适用于统计学领域的时间序列研究。