Naïve forecasting method used previous period actual value as current year forecast.

Its formula for forecasting is given below.

In moving average forecasting technique, the forecast is equal to the mean of all past data. It is denoted by MA and formulae for calculation is given below.

For 3 year moving average which is denoted by MA (3) the formula simplifies to:

In weighted moving average forecasting technique, the forecast is based on proportion relevance of each component. It is denoted by WMA and formulae for calculation is given below.

For 3 year weighted moving average with weights 0.3, 0.5 and 0.2, (denoted by WMA (3)) the formula simplifies to:

The formula for Exponential Smoothing Forecast assigns more weight to recent demands as compared to older demands. It is denoted by ES and formula for calculating future forecast is given below.

For alpha equal to 0.1, it denoted as ES (a=0.1) and its simplified version of the formula is given as:

Trend analysis is commonly known as regression analysis. It is used to estimate the relationship between demand/sales and time by fitting an equation to given data. 

Mean Absolute Deviation (MAD)

Mean Absolute Deviation (MAD) is the average of the absolute values or error (error is the difference between forecasted and actual value). It is used for calculation of demand/sales variability. the formula for MAD is given by:

Mean Square Error

Mean Square error (MSE) is the average of squared to error values (error is the difference between forecasted and actual demand value). it is used for the calculation of demand/sale variability. The formula for MSE is given by:

Mean Absolute Percentage Error

Mean absolute percentage error (MAPE) is the average of error divided by actual. It is also used for the calculation of demand/sale variability. The formula for MAPE is given by: