We present adapted SETAR (self-exciting threshold autoregressive) model, which enables simultaneous estimation of nonlinearity and unobserved time series components. This model was tested on real Lithuanian harmonised consumer price index (HCPI) time series, covering the period from January 1996 to December 2009. The results show that adapted SETAR model is able to capture features of the real time series with complex nature. ARIMA model has also been used for the same time series for the comparison. Evaluated models and results of the comparison are presented in this work.