Improving time series modeling by decomposing and analyzing stochastic and deterministic influences
We present an approach to decompose time series into components. The obtained components allow understanding stochastic and deterministic influences. By knowing such influences we can estimate models with high accuracy. A new measure evaluates the behavior of the predicted and expected observations. Experiments confirmed improvements by modeling stochastic/deterministic influences.
time series decomposition stochastic component deterministic component empirical mode decomposition recurrence plot