Malaysia is one of the top countries that produces natural rubber and was ranked sixth place globally. The earnings from natural rubber products are making billions of ringgit for the country. However, over the past years the natural rubber production in Malaysia has been inconsistent and the deficiencies in the production can affect Malaysia’s economy. Therefore, it is important for relevant agencies and departments to understand the patterns and trends of natural rubber production in Malaysia besides having the ability to forecast. Hence, the integrated autoregressive moving average (ARIMA), seasonal autoregressive moving average (SARIMA) and the seasonal Holt-Winter’s model were being considered for the purpose of modelling and forecasting this study. The forecast accuracy criteria used to evaluate the performance of the models are the root mean square error (RMSE) and mean absolute percentage error (MAPE). The results showed that the seasonal Holt-Winter’s model appeared to be the best model as it yielded the lowest RMSE and MAPE values. The seasonal Holt-Winter’s model, however, is not a good choice of model as it was unable to forecast six months ahead values. On the other hand, the SARIMA model had a better forecast ability when forecasting the values for the same duration. Therefore, the SARIMA model is taken to be the model in forecasting the natural rubber production in Malaysia for that period. This study has shown that the best fit model that fulfil all the forecast accuracy criteria may not have the best forecast ability.