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#ActualMakers_F

Posted 03 August 2013 - 06:33 AM

Well, the answer to your title's question is simple: no.
If you have no guarantees of how the data is collected and what phenomenon you are observing, there is absolutely no reason for which past data describe a future event or a model.
Since you do not have a model you can not use any method to infer future events.
You can notice a pattern in which whenever a bird sings, a dolphin in the ocean is jumping. But since this two actions are not correlated,  if you'll use it you'll have a wrong (or better, not reliable) prediction.
If you know the domain of the problem instead, you can start to build some rules (there are already some tools to do this. Search for "data mining", some programs do exactly want you want, they find patterns in data)

TL;DR
Without a knowledge of the domain you are observing, you can find patterns, but they do not mean anything and you can not safely use them to infer future events.
(Note: it can be that the data you are looking at actually belongs to a specific domain with some patterns, even if you don't know it. In that case you can use the pattern to predict future events, but since you do not know that the data belong to a specific domanin, you should not use them.)

@Alvaro: I'm not really informed about data compression, but i think the goal is to find the more common pattern and "replace" it with a shorter identifier. At least in the old days. It is not about predicting data that is not present. Am i right?


#1Makers_F

Posted 03 August 2013 - 06:32 AM

Well, the answer to your title's question is simple: no.
If you have no guarantees of how the data is collected and what phenomenon you are observing, there is absolutely no reason for which past data describe a future event or a model.
Since you do not have a model you can not use any method to infer future events.
You can notice a pattern in which whenever a bird sings, a dolphin in the ocean is jumping. But since this two actions are not correlated,  if you'll use it you'll have a wrong (or better, not reliable) prediction.
If you know the domain of the problem instead, you can start to build some rules (there are already some tools to do this. Search for "data mining", some programs do exactly want you want, they find patterns in data)

TL;DR
Without a knowledge of the domain you are observing, you can find patterns, but they do not mean anything and you can not safely use them to infer future events.
(Note: it can be that the data you are looking at actually belongs to a specific domain with some patterns, even if you don't know it. In that case you can use the pattern to predict future events, but since you do not know it, you should not use them.)

@Alvaro: i'm not really informed about data compression, but i think the goal is to find the more common pattern and "replace" it with a shorter identifier. At least in the old days. It is not about predicting data that is not present. Am i right?


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