Tourism demand directly affects the decisions of government and private sectors on their investments for the software/hardware facilities of tourism industry. If the demand forecast is seriously overstated, high levels of investments in transportation and accommodations can result. Conversely, an area which underestimates its tourism potential will develop less capacity, discouraging some tourists from visiting. Therefore, the ability to accurately forecast tourism demand can be very beneficial in their decision making. In this article, we investigate various forecasting methods such as naive method, trend curve analysis, time series models, and econometrics models to construct forecasting models for tourism forecasting. We also consider several well-known model selection criteria for the selection of these candidate forecasting models. We hope that these research conclusions can be of use to the policy decisions of government and private sectors for their future planning on tourism industry.