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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, .
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.
