An analysis of the price fluctuations of goose industry during the 459 week period commencing in 1998 and ending in 2006 was carried out in order to unveil the statistical characteristics of goose prices and to forecast the prices. The time series analysis method was considered appropriate because it is easy for goose farmers to understand and ensures predication accuracy. Several important findings are noted in the present study: 1. The average sale price per goose was NT$52.34 and NT$ 33.33 per kilogram. A correlation coefficient was r=0.22. 2. The time series becomes relatively stable after the first order difference by the augmented Dickey-Fuller test. 3. The models are proposed below: a. The regression model for estimation of goose price is: SGP=0.9564190856(superscript *)SGP(-1) + 0.5227434296(superscript *)D(SGP(-1)) + 0.09164880255(superscript *)D(SGP(-2)) + 0.05546035066(superscript *)D(SGP(-3)) + 0.1797128706(superscript *)D(SGP(-4)) + 2.288827474. b. The regression model for estimation of goose meat price is: GP=0.9580367044(superscript *)GP(-1) + 0.5677031589(superscript *)D(GP(-1)) + 1.392161044. In summary, the results of the present study demonstrate that: 1. It remains to be seen whether farmers should still be committed to the goose industry. Its history shows big fluctuations in its prices. Goose farmers should be aware that price fluctuations could have a severe impact on the movement of the labor force as well as the quality of their decision-making. For example, when the price falls, the labor force should be allowed to shrink or move to work in other areas such as the cleaning and disinfection of farm premises for preparation of the next flock. 2. The goose price is likely to be affected by the prices over the previous four periods whereas the goose meat price might be influenced by the price in the previous day. Goose farmers should take into account the recent movement in both the goose price and meat price while making prediction.