What is more efficient, autocorrelation or SSA?

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What is more efficient in extracting the pattern in a time series analysis, autocorrelation or singular spectrum analysis?
 
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Define "efficient" - then you will have your answer.
What does the method have to achieve and under what constraints?

i.e. the fastest (quickest real time) method is to look at the data and guess.
 
Simon Bridge said:
Define "efficient" - then you will have your answer.
What does the method have to achieve and under what constraints?

i.e. the fastest (quickest real time) method is to look at the data and guess.
By efficient, I meant the ability of the algorithm to get all possible information about the spectral components of the time series. Real time analysis is an important concern too.
 
Well I doubt either approach will get "all possible" information... they are both approximate methods.
Which is faster in real time will depend on the target and the data. But here you have the ability to get a quantitative assessment... i.e. by working out the number of machine cycles needed for each method.

Notice, I am not answering your question so much as trying to get you to think about it more clearly so you can answer it yourself.

There is a reason there is more than one way to skin this particular cat.
I suspect the short answer is that neither is intrinsically more efficient than the other.
 

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