# Modelling in Time Series Object

For discussions related to modeling, machine learning and deep learning. Related packages include `caret`, `modelr`, `yardstick`, `rsample`, `parsnip`, `tensorflow`, `keras`, `cloudml`, and `tfestimators`.

the time series model (which doesn't have periodic component) can be decomposed to
Y = T + S + R
Y = T.S.R
Y = T.S + R
Y = T + S.R (T = trend, S = Seasonal, R = Random)
But forecast package gives only additive and multiplicative models but not the combination
I want to try all 4 combinations and take the best fit. How can I do it and compare ?

The additive decomposition is the most appropriate if the magnitude of the seasonal fluctuations, or the variation around the trend-cycle, does not vary with the level of the time series. When the variation in the seasonal pattern, or the variation around the trend-cycle, appears to be proportional to the level of the time series, then a multiplicative decomposition is more appropriate. Multiplicative decompositions are common with economic time series.

An alternative to using a multiplicative decomposition is to first transform the data until the variation in the series appears to be stable over time, then use an additive decomposition. When a log transformation has been used, this is equivalent to using a multiplicative decomposition on the original data \dots