Time series decomposition works by splitting a time series into three components: seasonality, trends and random fluctiation. To show how this works, we will study the decompose( ) and STL( ) functions in the R language.
Extracting Seasonality and Trend from Data: Decomposition Using R
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, lições, linguagens de programação, materiais ensino, materiais para profissionais
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Uma excelente descrição da decomposição clássica com Python e R.
Understanding Decomposition
Decompose One Time Series into Multiple Series
Time series decomposition is a mathematical procedure which transforms a time series into multiple different time series. The original time series is often split into 3 component series:
- Seasonal: Patterns that repeat with a fixed period of time. For example, a website might receive more visits during weekends; this would produce data with a seasonality of 7 days.
- Trend: The underlying trend of the metrics. A website increasing in popularity should show a general trend that goes up.
- Random: Also call “noise”, “irregular” or “remainder,” this is the residuals of the original time series after the seasonal and trend series are removed.
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