[SciPy-User] OT: advice on modelling a time series
Fri Mar 12 09:46:33 CST 2010
While not directly Python related I am always impressed with the
quality of scientific advice available on this list, and was hoping I
could receive some...
I have a limited amount of an experimentally obtained time series from
a biological system. I would like to come up with a generative model
which would allow production of large quantities of data with
statistics as similar as possible to the experimental data. The time
series represents a position, and I am particularly interested in
transient high velocity/acceleration events (which are often not very
visible by eye in the position trace), so ideally any model should
reproduce those with particular care.
An example plot of a small section of the data (pos vel and acc) (1s)
is available here:
If it makes any difference it is sampled at 4kHz. I tried fitting a
basic autoregressive model. An order 38 model reproduced the position
signal visually quite well, but velocity and acceleration were far too
regular. I tried fitting one to the velocity, but I think the events
of interest are too far apart in bins so the order required is too
So, could anyone point me to anything that would be helpful in python
(so far I did the AR with a matlab package I found)? Also any
suggestions for how to proceed would be great - other than reading the
wikipedia article I am completely new to this type of AR modelling. So
far the only ideas I have involve either downsampling the signal (to
try to reduce the order of AR model needed), or splitting it in
frequency to low f/high f components and attempting to model them
separately then recombine. Do either of these seem sensible?
Is it likely some non-linear model would be required (pos,vel and acc
all have high kurtosis), or are normal AR models capable of recreating
this kind of fine structure if tweaked sufficiently?
Thanks in advance for any pointers,
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