Forecasting function
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Hi everybody,
I modified a function to forecast the value of a possible time series k steps ahead. The function is:
function yf = forecasting(y,order,k)
y = iddata(y');
[N, ny, nu] = size(y);
zer = iddata(zeros(k,ny),zeros(k,nu),y.Ts);
yz = [y; zer];
% Build model
mb = ar(y,order);
% Perform prediction
yft = predict(mb,yz,k,'e');
% Keep only predicted time series
yff = yft(N+k);
yf = yff.OutputData;
In the "main" file I tried to predicted the value of a sine, the result are pretty good:
x = 0:0.1:30;
y = sin(x);
ndata = length(y);
y = y';
ts = 1;
z = iddata(y,[],ts);
na = 2;
nc = 2;
m1 = ar(z,na);
order = 6;
k = 4;
y = y';
for ii = 12:ndata
yf(ii,1) = forecasting(y(1:ii),order,k);
end
figure(1)
plot(y)
hold on
plot(yf,'r')
y = y';
for gg=1:(ndata-4)
error(gg,1) = y(gg+4,1)-yf(gg,1);
end
figure(2)
plot(error)
However is there a way to find and predict the value for x > 30?
Thank you
Mario
1 Commento
Abolfazl Nejatian
il 10 Dic 2020
Modificato: Abolfazl Nejatian
il 10 Dic 2020
this is my forcasting code which is allow you predict your time series data with LSTM, CNN, and MLP Networks.
Risposte (5)
Rajiv Singh
il 22 Lug 2011
Give the following function a try. Let me know if it works.
function YP = forecast(model,data,K, Init)
%FORECAST Forecast a time series K steps into the future
%
% YF = FORECAST(MODEL,DATA,K)
%
% DATA: Existing data up to time N, an IDDATA object.
% MODEL: The model as any IDMODEL object, IDPOLY, IDSS, IDARX or IDGREY.
% K: The time horizon of forecasting, a positive integer with the number of
samples
% YF: The forecasted output after time N, an IDDATA object with output
% only, covering the time span N+1:N+K.
%
% YF = FORECAST(MODEL,DATA,K, INIT)
% wehere INIT is 'z' or 'e' allows specification of initial conditions (at
% time = Data.SamplingInstants(1)).
%
% See also idmodel/predict, which computes a fixed horizon prediction
% along the existing data record.
[N, ny] = size(data); % assume data is iddata
Mss = idss(model);
ord = size(pvget(Mss,'A'),1);
if ord>N
error('Forecast:TooFewSamples','The data should contain at least %d
samples.',ord)
end
if nargin<4, Init = 'e'; end
yp = zeros(K,ny);
mp = getPredictor(Mss);
[Ap,Bp,Cp] = ssdata(mp);
if Init=='z'
xt = ltitr(Ap, Bp, data.y); % use zero init
x0 = xt(end,:);
else % Init == 'e'
[A1,B1,C1,D1,K1] = ssdata(Mss);
x00 =
x0est(data.y,A1,B1,C1,D1,K1,size(C1,1),size(B1,2),250e3,eye(size(C1,1)));
x0 = ltitr(Ap,Bp,data.y,x00); x0 = x0(end,:);
end
u = [data.y(end,:); zeros(1,ny)];
for ct = 1:K
xt = ltitr(Ap, Bp, u, x0);
x0 = xt(end,:);
yp(ct,:) = (Cp*x0.').';
u = [yp(ct,:); zeros(1,ny)];
end
YP = data; YP.y = yp; YP.u = []; YP.Name = '';
YP.UserData = []; YP.Notes = '';
YP.Tstart = data.Ts*(N+1);
%------local function -----------------------------------
function mp = getPredictor(sysd)
[A,B,C,D,K] = ssdata(sysd);
Ts = sysd.Ts;
Ny = size(D,1);
mp = idss(A-K*C, [K B-K*D], C, [zeros(Ny), D], zeros(size(A,1),Ny), 'Ts',
Ts);
mp.InputDelay = [zeros(Ny,1); sysd.InputDelay];
0 Commenti
Rajiv Singh
il 3 Ago 2011
Mario, You are not using forecast correctly. The idea is to NOT call it in a loop (that defeats the purpose). You should simply do:
YP = forecastRaij(model,data,limit,'z');
The large prediction error (see model.es.FPE) means that the model is probably not good. One step ahead prediction is not a big task even for a poor model (just try the model y(t) = y(t-1) which wouldn't do too bad either).
Finally note that your model is a stable autoregressive process with fading memory. Any forecasting using such a model would eventually (rather quickly for your model) go to zero values. If you start with zero initial values then forecasted values would also be zero for an AR process because it has finite memory (two samples in your case). You might need an ARMA model (rather than an AR model) and perhaps a persisting process (root of "A" polynomial at unity) as in the case of seasonal ARMA models.
I would recommend investing some time into understanding what prediction and forecasting are about so that you can make educated modeling assumptions.
1 Commento
Mohamed Hussain
il 9 Ott 2011
Dear Rajiv,
When i used the function forcast the following error messages appeared when i used both linear and non linear models:
Linear Model:
??? Undefined function or method 'forecast' for
input arguments of type 'idpoly'.
Non Linear Model:
??? Undefined function or method 'forecast' for
input arguments of type 'idnlarx'.
is there something wrong with the syntax or with function definition and initial conditions ?
Thank your for your help
Mohamed Hussain
Mario
il 4 Ago 2011
1 Commento
Rajiv Singh
il 4 Ago 2011
Choosing the right model structure for a problem is a different, significantly more complex, matter. I would advise getting back to the basics of the process that generates this data. Understanding the nature of your system and that of the data collection process would throw some light on it. A quick inspection of your data doesn't tell much (although I doubt if a lower order linear model would capture the behavior).
You should start a different thread for any questions related to use of software for model building.
Rajiv Singh
il 17 Mar 2012
In R2012a, you can also use a new "FORECAST" function. See help on "idParametric/forecast".
1 Commento
Xiang
il 23 Giu 2014
hi,I am using Matlab R2014a, but I cant find the function forecast, could you help me? thanks!
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