"Automated short term gas production forecasting using machine learning/big data/data mining"
Hi,
First let me introduce quickly. I'm Maurits Freriks, student Business Analytics of VU Amsterdam. Recently I'm doing an internship for 3 months. I've to investigate if it's possible to automated short term gas production. With other words: An predicition based on historical data. I do have a litte experience with rapid miner but not that much. And first of all I'm wondering if this problem could be solved with Rapid Miner?
What I've done so far:
- I've received an dataset with historical datavalues of the last 3 years. The data comes from measure points for example: The flow of the amount of gass on a specific time serie, degrees, pressure etc.
- I've devided this dataset in a smaller dataset containing only 1 month of data.
- I've built a process with the small dataset and operator polynomial regression. I've received a solution with some coeffincients but if i test this to to total data set, the deviation was to high so the formule was useless.
Now my question is before spending more and more time in Rapid Miner, if there are some recommendations which operators I've to use. And for example do I have to make a testset and trainingset. If yes, is it right if I devided the total dataset into 80% training an 20% testset.
I appreciate your attention, effort and time. Hopefully someone could help me out!
And by the way: Sorry for my english!!
With kind regards,
Maurits Freriks