  Accurate and robust forecasting methods for univariate time series are very
important when the objective is to produce estimates for a large number of time
series. In this context, the Theta method called researchers attention due its
performance in the largest up-to-date forecasting competition, the
M3-Competition. Theta method proposes the decomposition of the deseasonalised
data into two "theta lines". The first theta line removes completely the
curvatures of the data, thus being a good estimator of the long-term trend
component. The second theta line doubles the curvatures of the series, as to
better approximate the short-term behaviour. In this paper, we propose a
generalisation of the Theta method by optimising the selection of the second
theta line, based on various validation schemes where the out-of-sample
accuracy of the candidate variants is measured. The recomposition process of
the original time series builds on the asymmetry of the decomposed theta lines.
An empirical investigation through the M3-Competition data set shows
improvements on the forecasting accuracy of the proposed optimised Theta
method.
