I have recently been working with time-series data extensively and looking
at different ways to model, classify, and predict different types of
time-series.
One algorithm I have been playing with is called SAX (
http://www.cs.ucr.edu/~eamonn/SAX.htm). It is a very straightforward
algorithm (basically windowed mean with no overlap, then quantize into M
levels), and I have implemented a rough version using numpy. Despite its
simplicity, it is shown as being an effective data dependent transform,
similar in some ways to the DWT.
I think this algorithm would be a nice tie-in to sklearn, which could allow
for more of sklearn's algorithms to be used on time-series type data. Also,
the algorithm makes very strong claims about indexing massive datasets,
finding similarities and outliers, which are all things I am planning to
explore in the future.
I know that FastICA is under decomposition, and is often seen in a
time-series context - would symbolic aggregation fall into the
"decomposition" camp as well? Is sklearn even the right place for this?
Kyle
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