Comparative Analysis of Four Time-Series Models in an Effort to Determine The Optimal Forecasting Results

Forecasting is one of the company's efforts, which is the decision making for the strategy carried out. A good company will predict the production of products that will be achieved, for the future and can help solve the problem of fluctuating conditions which generally often become a problem in the company. Forecasting demand (demand), is very helpful for companies to consider in carrying out the production process in the next several periods. Furthermore, to find out what method is suitable for the forecasting method, it is used by knowing the patterns and historical data. Accuracy in using the right forecasting method is expected to assist the company in determining the level of production in accordance with consumer demand in the future. Time-series models are methods that predict the future using historical data. Time-series models make the assumption that what happens in the future is a function of what has happened in the past. The advantage of time-series models is that they can see what has happened over a period of time and use a series of past data to make predictions. The application of the right and directed forecasting method can support the company to make optimal sales in order to achieve the maximum level of profit.