MsFormulaKind

Description

The MsFormulaKind enumeration is used to determine the method that is used to calculate a model.

It is used by the following properties and methods:

Available Values

Value	Brief description
-1	None. Method is not determined.
0	Deterministic. Determinate equation.
1	LinearRegression. Linear regression.
2	ExponentialSmoothing. Exponential smoothing.
3	Arima. ARIMA.
4	User. Custom method.
5	CurveEstimation. Universal trend.
6	SlideSmoothing. Moving average.
7	MedianSmoothing. Median smoothing.
8	HodrickPrescottFilter. Hodrick-Prescott filter.
9	NonLinearEquations. Equation system.
10	InterindustryBalance. Input-output balance.
11	ErrorCorrectionModel. Vector error correction model.
12	ECMEquation. Equation in the vector error correction model.
13	NonLinearOptimization. Non-linear optimization.
14	Aggregation. Aggregation.
15	NonLinearRegression. Non-linear regression.
16	GreyForecast. Grey forecast.
17	LRXFilter. LRX filter.
18	CointegrationEquation. Error correction model.
19	BandpassFilter. Baxter-King filter.
20	DetermAggregation. Aggregation that is calculated as a determinate equation.
21	SLS2. Linear regression (instrumental variables estimation).
22	FillGaps. Missing data treatment method.
23	Cumulative. Cumulative method.
24	Census2. X11 smoothing method (seasonal decomposition and adjustment method). NOTE. The X11 method is supported only in Windows.
25	Collapse. The Collapse (series calculation) model (aggregates data from the lower level to the upper level by the calendar dimension).
26	Interpolate. The Interpolation model (disaggregates data from the upper level to the lower level by the calendar dimension).
27	Splice. Series splice.
28	LinearEquations. System of simultaneous equations.
29	BinaryRegression. Binary regression.
30	PointwiseCollapse. The Collapse (pointwise calculation) model (executes pointwise aggregation of the lower level data to the upper by the calendar dimension).
31	CrossDimensionAggregation. Aggregation by group, selection or parameter, that uses the missing data treatment method.
32	PooledModel. Panel data regression.
33	ValueAtRisk. The Value-At-Risk model.
34	Census1. The Census1 smoothing method.
35	TargetOptimization. Criterion function.
36	R. R model.
37	MatrixAggregation. Matrix aggregation.
38	Validation. Data validation model.
39	UserOptimization. Custom optimization model.

Comments

Mathematical description of methods is given in the Library of Methods and Models section.