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（time series）分解相关研究，并梳理了四种经典时序分解方法的实现方案，分别为经典分解（Classical decomposition）、X11、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）。完成时序分解后，本研究基于南非国家电力公司（Eskom）可向南非配送的月度电力时序数据集，开展基于分解的预测任务。本次研究全程采用R Studio完成，同时阐释了时间序列的组成成分与移动平均法（moving averages）。
本研究第二部分采用了其他公开数据集及R语言内置数据集，用于演示时间序列的组成成分与移动平均法。随后，第三、第四部分复用了上述Eskom月度电力时序数据集，分别用于实现各类时序分解方法、分析各方法的随机成分，开展基于分解的预测任务，并计算四种不同预测方法的预测精度。