Naive forecast

MASE is less than one if it arises from a better forecast than the average one-step Naive forecast computed in-sample. The Naive model uses the last observation of the time series directly as the forecast. Conversely, it is greater than one if the forecast is worse than the average one-step Naive forecast computed in-sample. 

It is also good practice to compare the statistical forecast to a naive forecast. (The naive forecast is a simple technique where the forecast equals the volume of goods sold in the prior forecasting period.) Naive forecasts, in some situations, can be surprisingly difficult to beat, yet it is very important that the organizations ensure that software and a statistical modeler improve on the naive model. The focus needs to be on continuous improvement. If the software, modeler is not able to do this, it makes sense to implement better software, improve the skills of the modeler, or just use the naive model as a baseline forecast. 

As shown with the minimum-variance method, solution of implementing traditional portfolio optimization is often expressed in highly concentrated portfolio. One alternative to overcome such difficulties is to use the equal weighting approach. This method is often considered as a naive diversification strategy which attempts to capture some of the potential gains from international diversification ([14], p. 229). Its major advantage is robustness as it does not require return or volatility forecasts, which is also one of the most important reasons for popularity of the EQW approach. Despite its simplicity and popularity, EQW certainly has some pitfalls. One of the most obvious is the fact that it does not account for volatilities and correlations between assets. 

Makridakis (1998) states that a wide variety of forecasting methods are available to management and range from the most naive methods to highly complex approaches, such as neural nets and econometric systems of simultaneous equations. 

There are many types of forecasting methods in common use. The selection of the method to use depends on the industry of a firm and the length of time that is being forecasted. The simplest forecasting technique is the naive method. It simply states that demand this next period will be what demand was the last period. This is not a useful method for most industries. It is however effective in some industries, such as the fast food industry. In that industry the store manager predicts sales for the next Monday based on the sales of the prior Monday and orders an appropriate amount of food and schedules the necessary staff... 

Under the naive method, the forecast for month 7 = Fj = Demand for month 6 = 340. 

For the sake of illustration, the forecasts for the first quarter of 2011, using the Naive method and the 4-quarter moving average method are also given in the following ... 

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