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In time-series forecasting managers can forecast future demand and sales based on data from past projects. The time-series forecasting model assumes that the all the information required for forecasting the demand is related to its past data and demand patterns exhibited over a range of time. Basically, any forecast can be generated by analyzing the patterns exhibited in adequate amount of data. There are different types of time-series forecasting models.

1. The Nave approach: In the nave approach, demand for the next period is assumed to be same in the most recent period. This method can be used in economic and financial time series analysis. It can be used to forecast demand for mature products having level or seasonal demand without a trend.

2. Exponential smoothing: It is a weighted moving average forecasting method. This method is suitable to determine forecast for most of the industries.

3. Moving average: This method uses a number of historical data to determine the forecasted value. This method should be used to predict the demand of industry containing mature products.

4. Trend Projection: This technique fits a trend line to a series of historical data points and then projects the slope of the line into the future for medium- to long-range forecasts.

There is also the Associative. The Associative model assumes that the one variable is associated with another variable. It tries to find out that relationship and then uses it to forecast the other variable. A good example might be the advertising budget of a product and sales. There's one main type of forecasting model associated with the Associative model and it's called Linear Regression. Linear regression is the technique that utilizes the relationship between two or more variables to predict the sales/demand forecast. It can used to predict things like the stock market price and consumer price index.