procedure ARARForecast(const Data: TVec; const Phi: TVec; const Filter: TVec; const tau: Integer; const l1: Integer; const l2: Integer; const l3: Integer; const SMean: Double; const N: Integer; const aResult: TVec; const StdErrs: TVec; out RMSE: Double);
Forecast time series by ARAR.
| # | Name | Description |
|---|---|---|
| 1 | Data | Defines original time series. |
| 2 | Phi | Defines ARAR model Phi coefficients (phi[0],phi[1],phi[2],phi[3]). |
| 3 | Filter | Defines memory shortening filter, obtained from memory-shortening operation. In case no memory-shortening is performed, set filter to 1.0 by using Filter.SetIt([1.0]). |
| 4 | tau | Defines memory-shortening optimal lag, obtained from memory-shortening operation. In case no memory-shortening is performed, set it to 1. |
| 5 | l1 | Defines optimal lag for phi[l1] (see equation above). |
| 6 | l2 | Defines optimal lag for phi[l2] (see equation above). |
| 7 | l3 | Defines optimal lag for phi[l3] (see equation above). |
| 8 | SMean | Defines memory-shortened series mean. |
| 9 | N | Defines number of forecasts. |
| 10 | aResult | Returns forecasts. Size and complex properties of Result are adjusted automatically. |
| 11 | StdErrs | Returns forecasts standard errors. Size and complex properties of StdErrs are adjusted automatically. |
| 12 | RMSE | Returns fit root mean square error (RMSE). |
Result: stored in self (calling object)
Remarks:
Forecast time series values by using ARAR model, defined by the following relation:
Examples
Uses MtxExpr, StatTimeSerAnalysis, Math387;
procedure Example;
var timeseries,s,filter,phi: Vector;
forecasts,stderrs: Vector;
l1,l2,l3,tau: Integer;
s2,rmse: double;
begin
timeseries.LoadFromFile('deaths.vec');
// #1: shorten series
ShortenFilter(timeSeries,s,tau,Filter);
// #2 : fit ARAR model on shortened series
ARARFit(s,Phi,l1,l2,l3,s2,13);
// #3: forecast 100 values by using ARAR fit parameters
ARARForecast(timeseries,Phi,Filter,tau,l1,l2,l3,s.mean,100,forecasts,stderrs,rmse);
end;