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The decomposition of time series is a statistical method that deconstructs a time series into notional components. There are two principal types of decomposition which are outlined below. ==Decomposition based on rates of change== This is an important technique for all types of time series analysis, especially for seasonal adjustment. It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behaviour. For example, time series are usually decomposed into: *the Trend Component that reflects the long term progression of the series (secular variation) *the Cyclical Component that describes repeated but non-periodic fluctuations *the Seasonal Component reflecting seasonality (seasonal variation) *the Irregular Component (or "noise") that describes random, irregular influences. It represents the residuals of the time series after the other components have been removed. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「decomposition of time series」の詳細全文を読む スポンサード リンク
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