Hi Helge,
thanks for the reply.
I have tried using first derivative to fit hmm. This is the results
0th hidden state
('mean = ', array([ 0.08534182, 0.07491009]))
('var = ', array([ 1.45029737, 1.23234594]))
1th hidden state
('mean = ', array([-0.25764837, -0.24770928]))
('var = ', array([ 0.06077981, 0.06085699]))
2th hidden state
('mean = ', array([ 0.10585516, 0.10645033]))
('var = ', array([ 0.04375404, 0.04436854]))
The problem is that hmm seems to separate the states by variance, because
in 0th state, I have big rise and down portions. This is similar to the
stock example here
http://scikit-learn.org/stable/auto_examples/applications/plot_hmm_stock_analysis.html#example-applications-plot-hmm-stock-analysis-py
The 4th state identified is based on variance also. So how can I force the
state detection based on mean instead?
On Mon, Aug 26, 2013 at 1:31 AM, Helge Reikeras <[email protected]>wrote:
> Hi
>
> 1. From the source code it looks like predict is just a wrapper around
> decode. I see predict only returns the state sequence with largest
> likelihood while decode also returns the actual value of the log
> likelihood.
>
> 2. I'm no expert on modelling financial data but it sounds like you are
> mostly interested in the price change so using the first derivative (and
> maybe 2nd and higher derivatives) seems reasonable. As I guess the data is
> noisy you should probably compute these using something like SavitzkyGolay
> filter:
>
> http://wiki.scipy.org/Cookbook/SavitzkyGolay
>
> Then the sign of the value of the first derivate feature in the state
> distribution mean will tell you if the state represent "up" or "down"
> (apply a distance from 0 threshold for "neutral") and the absolute value
> will give you the magnitude.
>
> Regards,
>
> --
> Helge Reikeras
>
> On Sunday 25 August 2013 at 4:41 PM, Shuo Wang wrote:
>
> Hi,
>
> I have two questions regarding the hmm model from a user's perspective,
>
> 1. what is the difference between method decode and predict, they seems to
> be having the same description
>
> 2. If i fit a financial time series into hmm, hmm seems to automatically
> detect states based on mean and variance, so if I want it to detect states
> related to regimes(up, down and neutral trends), I have to transform price
> into binary time series, such as 1 indicates current minute is a rise
> compare to previous minutes, -1 indicates current minute is a drop compare
> to previous minutes, 0 is unchanged. But if I transform my price info into
> binary information, I loss magnitude information. How can I keep the
> magnitude information, yet still let the hmm detect up, down and neutral
> trend, instead of mean and variance regimes.
>
> --
> 王硕
> 邮箱：[email protected]
> Whatever your journey, keep walking.
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邮箱：[email protected]
Whatever your journey, keep walking.
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