Questions about Time Series Example Sets
Hi,
I'm relative new to RM, and Machine Learning in generell. I tried to solve this problem myself over the course of the last week, but I just have to admit, that I am not making any progress. So I hope that I can get at least a clue, how I can solve this problem. Everytime I think I am on my way to the solution, there comes a point in time, where I get stuck or learn, that I was just wrong.
My first question is about time series. I have several Datasets in the form of time series. Now I wanted to train a model using those, but I am not sure what I need to do to achieve this. My understanding is, that all of those datasets should be one example set, where each row is one time series. That would mean I need something like a nested example set, which I don't think exists, right? So I tried to loop each example sets (or time series on that note) through the training. But the result I get is a model for each example set.
Is there a way that I can train (and test) one single model for all example sets?
In the end I thought to use a Random Forest, but would like to try some other models.
And my second question is, how do I add some kind of "global", time independent attribute to an example set? By adding another attribute in the form of column, wouldn't that imply that this attribute is time dependent?
I hope, this is not too much to ask for. I would be already grateful, if someone could hint me to the right direction, since I am at a point, where I have absolutly no idea how to solve those problems.
Best regards,
Joe
I'm relative new to RM, and Machine Learning in generell. I tried to solve this problem myself over the course of the last week, but I just have to admit, that I am not making any progress. So I hope that I can get at least a clue, how I can solve this problem. Everytime I think I am on my way to the solution, there comes a point in time, where I get stuck or learn, that I was just wrong.
My first question is about time series. I have several Datasets in the form of time series. Now I wanted to train a model using those, but I am not sure what I need to do to achieve this. My understanding is, that all of those datasets should be one example set, where each row is one time series. That would mean I need something like a nested example set, which I don't think exists, right? So I tried to loop each example sets (or time series on that note) through the training. But the result I get is a model for each example set.
Is there a way that I can train (and test) one single model for all example sets?
In the end I thought to use a Random Forest, but would like to try some other models.
And my second question is, how do I add some kind of "global", time independent attribute to an example set? By adding another attribute in the form of column, wouldn't that imply that this attribute is time dependent?
I hope, this is not too much to ask for. I would be already grateful, if someone could hint me to the right direction, since I am at a point, where I have absolutly no idea how to solve those problems.
Best regards,
Joe