NO specifies forecasting the last cycle. It is the default value.

YES specifies the forecasting of only the average value for one period of the cycle. That average value is then multiplied by factors to give the remaining points in that period. For example, when the last cycle has 24-hour periods, only an average hourly value is forecast, which is then multiplied by 24 hourly factors to give the value for each hour.

APPAUTO indicates that Oracle OLAP tests all of possible models and options for these models and chooses and uses the model that best fits the data. APPAUTO is the default value.

APPMANUAL indicates that Oracle OLAP uses the specified forecasting model when it determines that the data and other specified options are appropriate for that model. If it does not determine the overall appropriateness of the characteristics of the forecast, then it tests all of the possible models and options for these models and chooses and uses the model that best fits the data.

APPHYBRID indicates that, using the forecasting arguments that are specified in the ForecastDefinition as the base options, Oracle OLAP tests all of the possible models and options for these models and chooses and uses the model that best fits the data.

NO specifies that the methods are done using the original historical time series data. It is the default value.

YES specifies that optimization is done on the median smoothed data series, which results in more smoothed or baseline forecasts.

AUTOMATIC specifies that Oracle OLAP should determine and use the method that is the best fit for the data. It is the default value.

LINREG specifies the linear regression method in which a linear relationship (y=a*x+b) is fitted to the data.

NLREG1 specifies the nonlinear regression method 1 in which a linear relationship (y=a*x+b) is fitted to a transformation of the original data; in this case, x=log(x) and y=log(y). This results in the development of a polynomial model between x and y(y=c*x^a).

NLREG2 specifies the nonlinear regression method 2 in which a linear relationship (y=a*x+b) is fitted to a transformation of the original data; in this case, x=x and y=ln(y). This results in the development of an exponential model between x and y(y=c*e^ax).

NLREG3 specifies the nonlinear regression method 3 in which a linear relationship (y=a*x+b) is fitted to a transformation of the original data; in this case, x=log(x) and y=y. This results in the development of a logarithmic model between x and y(y=a*log(x)+b).

NLREG4 specifies the nonlinear regression method 4 in which a linear relationship (y=a*x+b) is fitted to a transformation of the original data; in this case, x=1/x and y=1/y. This results in the development of an asymptotic curve (y=x/(a+bx)).

NLREG5 specifies the nonlinear regression method 5 in which a linear relationship (y=a*x+b) is fitted to a transformation of the original data; in this case, x=x and y=ln(y/(K-y)). This results in the development of an exponential asymptotic curve (y=cKe^ax/(1+ce^ax)).

SESMOOTH specifies the single exponential smoothing method in which the current estimate is taken as a geometrically weighted average of past values, and all future values are given this same value. This method is intended for short term forecasts of non-seasonal data.

DESMOOTH specifies the double exponential smoothing method in which the current estimate is taken as a geometrically weighted average of past values, and this is added to a trend term calculated by the same method. Single exponential smoothing is therefore applied to both the series and the trend term.

HOLT/WINTERS specifies the Holt-Winters method that is used on seasonal data, in which double exponential smoothing methods with trend damping are combined with multiplicative seasonal factors, which are estimated using single exponential smoothing.

Cycles are groupings of time periods that repeat through the time span of the data. For example, daily periods can be grouped into a weekly cycle and weekly periods can be grouped into a yearly cycle. In this case, the cycles are said to be nested, with the yearly cycle more aggregate than the weekly cycle, and the weekly cycle more detailed than the yearly cycle. By specifying cycles at a more detailed level, you allow OLAP to conduct a finer-grained search for factors that affect the data.

• To specify a single cycle, set an integer value (as a String) from 1 through 25000. The value indicates the number of periods into which to divide the cycle. For example, specifying 12 divides the cycle into 12 periods.

• To specify a series of nested cycles, set a series of up to six integer values (as String objects) enclosed in parentheses and separated by commas. Each value in the series is the number of periods in one of the nested cycles. The cycles are ordered from most aggregate to least aggregate. For example, specifying the value "(52,7)" indicates two cycles in which the most aggregate cycle is divided into 52 periods and each of those periods is divided into seven periods. In this example, the year is divided into 52 weeks, and each of those weeks is divided into seven days.

NO specifies to not smooth the data for the forecast. It is the default value.

YES specifies to smooth the data for the forecast.

TRNOSEA specifies to not seasonally adjust the data. It is the default value.

TRSEA specifies to transform using a filter that seasonally adjusts the data.

TRMPT specifies to transform using a moving periodic total (MPT) filter.