I want to use time-series prediction techniques in association with regression methods. But I am very much confused about how to construct the training and test sets for the model. Suppose I have a series as [2 4 6 7 10 11 12 13 14.15 16 17 18]. Assume choosing a delay of 2 and embedding dimension=3 . Let the training set be [2 4 7 10 11 12 13 14 15] and test set be [16 17 18]. Also, once the training is complete, the test values are entered as 16, 17, 18 one by one. Then at what timestep, I am predicting when i enter the value equal to 16. Please help me to figure out the problem?
See the two papers attached below. They may help. The short answer to your question is if the delay is 2 then if you input from time t1 then you are predicting t3. Hope this helps. Best wishes.
If you are interested in univariate time series data predictions, have a look in PSF algorithm and it's R Packages available at :
CRAN: https://cran.r-project.org/web/packages/PSF/index.html
GitHub: https://github.com/neerajdhanraj/PSF
How to use: https://www.researchgate.net/publication/304131481_PSF_Introduction_to_R_Package_for_Pattern_Sequence_Based_Forecasting_Algorithm
and
https://www.researchgate.net/publication/304580701_Introduction_of_seasonality_concept_in_PSF_algorithm_to_improve_univariate_time_series_predictions
For further details contact me at: http://www.neerajbokde.com
Article PSF: Introduction to R Package for Pattern Sequence Based Fo...
Article Introduction of seasonality concept in PSF algorithm to impr...
Hallo. You can follow my project
https://www.researchgate.net/project/Multiple-Kernel-Support-Vector-Regression-MKSVR
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