Forecasts of football scores
Contributor I
Hi all,
i'm new in this forum.
I am studying different models to predicting results of football matches. In my opinion it would be very useful to use time series to improve the predictive accuracy of this problem.
I would like to use functionality windowing in rapidminer such as in the financial forecasts (http://www.neuralmarkettrends.com/2010/03/30/rapidminer-5-0-video-tutorial-9-financial-time- series-modeling-part-1 /).
There is a problem: the variable to predict is a nominal variable [1, X, 2].
I have converted this variable in numeric by using the "nominal to numeric". Obviously the result of predicted variable is a real number between [-1932, 3637]. How should I interpret these results? is the correct approach to the problem or I can not deal with nominal variables in the time series in rapidminer?
Thank you very much
Best regards
i'm new in this forum.
I am studying different models to predicting results of football matches. In my opinion it would be very useful to use time series to improve the predictive accuracy of this problem.
I would like to use functionality windowing in rapidminer such as in the financial forecasts (http://www.neuralmarkettrends.com/2010/03/30/rapidminer-5-0-video-tutorial-9-financial-time- series-modeling-part-1 /).
There is a problem: the variable to predict is a nominal variable [1, X, 2].
I have converted this variable in numeric by using the "nominal to numeric". Obviously the result of predicted variable is a real number between [-1932, 3637]. How should I interpret these results? is the correct approach to the problem or I can not deal with nominal variables in the time series in rapidminer?
Thank you very much
Best regards
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0
Answers
you should NOT convert your label to numerical if it's really nominal! That changes from a classification task to a regression task, making the same much more difficult. You can do windowing with nominal attributes...
Greetings,
Sebastian
PS: Won't work. We already tried to forecast football results