Seasonal Autoregressive Integrated Moving Average (ARIMA) process of (0,1,2) x (0,1,1) x 6 that best fits a set of crop‑wise coconut yield data, in Bandirippuwa, Lunuwila is identified with­out using variance stabilization transformation. In this process the present value of the series may be described as a linear function of the past observation of the series and past disturbances. The physical factors such as rainfall, temperature, day length etc. are not required for this method, however the past crop figures in the estate is needed. While such model is useful for short term fore­casting, it also gives the upper and lower limits of the forecasts at a given probability. These intervals would provide the quantified information on the degree of duration of the forecasts.