# Trend and seasonality tests for a univariate time series

I am working with a batch of about 1000 univariate time series in R . For every time series, I have to perform following tasks , before deciding upon a model be it ARIMA, TAR or Holt Winter's Model

1. Trend Detection and its type , i.e. whether trend is deterministic or stochastic
2. Seasonality Detection and then deciding whether it is additive or multiplicative
3. Does the series needs transformation. If yes then what kind of transformation is required, i.e whether box-cox or logarithmic.

Currently I have to visualize every series and then take a call , are there any mathematical criterion available, which can reduce this effort

Also what are the other factors that I need to consider before deciding on which model to use