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How to decompose time series data into trend?
You may address it explicitly in terms of modeling the trend and subtracting it from your data, or implicitly by providing enough history for an algorithm to model a trend if it may exist. You may or may not be able to cleanly or perfectly break down your specific time series as an additive or multiplicative model.
Why are decomposition procedures used in time series?
Decomposition procedures are used in time series to describe the trend and seasonal factors in a time series. More extensive decompositions might also include long-run cycles, holiday effects, day of week effects and so on. Here, we’ll only consider trend and seasonal decompositions.
Which is the best decomposition for seasonal variation?
The seasonal variation increases as we move across time. A multiplicative decomposition could be useful. Here’s the plot of the data: The seasonal effects are usually adjusted so that they average to 0 for an additive decomposition or they average to 1 for a multiplicative decomposition. The first step is to estimate the trend.
How to iterate through the steps of decomposition?
A few programs iterate through the steps 1 to 3. For example, after step 3 we could use the seasonal factors to de-seasonalize the series and then return to step 1 to estimate the trend based on the de-seasonalized series. Minitab does this (and estimates the trend with a straight line in the iteration. The basic command is decompose.
How to choose the best time series decomposition?
To choose an appropriate decomposition model, the time series analyst will examine a graph of the original series and try a range of models, selecting the one which yields the most stable seasonal component. If the magnitude of the seasonal component is relatively constant regardless of changes in the trend, an additive model is suitable.
What can be removed from a decomposition model?
Beyond that, however, we need a more formal means for identifying and removing characteristics such as a trend or seasonal variation. As discussed in lecture, the decomposition model reduces a time series into 3 components: trend, seasonal effects, and random errors.
How does Shiskin decomposition of time series work?
The Shiskin decomposition gives graphs of the original series, seasonally adjusted series, trend series, residual (irregular) factors and the between month (seasonal) and within month (trading day) factors that are combined to form the combined adjustment factors.