Hi Atreyee,
The predict function from the "System Identificatoin Toolbox" can be used to perform multi-step ahead forecasting. This function computes the K-step-ahead output of an identified model using measured input-output data. Here is a sample code for the same:
% Assuming 'data' is your time series data
% Estimate an AR model from the data
Mdl = ar(data, 2);
% Perform the K-step ahead prediction
K = 144;
yp = predict(Mdl, data, K);
% Plotting the original data and the prediction
figure
plot(data.OutputData)
hold on
plot(yp.OutputData, 'r')
hold off
Here, ar is a model used to estimate an autoregressive model of order 2 from the data. The choice of model depends on the characteristics of your data and the specific requirements of your forecasting task. It is always a good idea to try multiple models and choose the one that gives the best performance on your validation data.
Refer to the following documentation to learn more about these functions:
- predict: https://www.mathworks.com/help/ident/ref/predict.html
- ar: https://www.mathworks.com/help/ident/ref/ar.html
Here are some examples for time-series prediction which might be useful:
- https://www.mathworks.com/help/deeplearning/gs/get-started-with-time-series-forecasting.html
- https://www.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-learning.html
- https://www.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-network-designer.html
Hope this helps!
