This paper presents a comparison between the moving average and the LULU smoothing techniques for time series analysis under the Autoregressive(AR) and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models.. Different methods are being used for smoothing time series data and other smoothing purposes. These methods include moving average, weighted moving average, exponential smoothing, double exponential smoothing, Kernel smoothers, median smoothers, non-linear state space approach and LULU smoothers among others. The comparison between the moving average and the LULU smoothing methods in this paper is performed using monthly inflation data in Ghana from 2008 to 2013 under the AR modeling and the GARCH modeling procedures. The results showed that ARLU (1, 2) model was optimal. This is an indication that the LULU smoothing of order 2 is more accurate in smoothing inflation rates as compared to the moving average smoothing method. It also revealed that the ARLU (1, 2) modelis the best for modeling and forecasting monthly inflation rates in Ghana over the study period. A one year out of sample forecast for the year 2014 by the ARLU (1, 2) model showed that in the short term there would be a consistent increase in the monthly inflation rates in Ghana for the year 2014.

Keywords : LULU smoothers, Moving average, AR model, GARCH model, Inflation rates, Ghana.