Where should this code live? In carbon complex event processor component or in Siddhi
Regards Suho On Wed, Apr 23, 2014 at 10:03 AM, Sriskandarajah Suhothayan <[email protected]>wrote: > Looks good for me please proceed. > > Suho > > > On Tue, Apr 22, 2014 at 6:25 PM, Seshika Fernando <[email protected]>wrote: > >> Hi, >> >> After researching on how to handle seasonality in regression, I have the >> following findings. >> >> 1. Can use dummy variables to capture seasonality. The user needs to add >> dummy variables to the input stream to capture and quantify seasonality in >> the regression equation. Therefore, this does not have to be explicitly >> implemented since it will be a user input to the generic regression >> function. >> eg:- If there is a quarterly pattern, user 3 dummy variables >> to denote the 4 quarters. (dummy variables are binary variables) >> >> 2. If seasonality is continuous (i.e. not discrete) for example >> sinusoidal or quadratic or exponential, then the regression equation ceases >> to be linear and we need to identify the functional form that fits the data >> set. Then using that functional form, the user needs to convert it to >> linear form, prior to sending the data set to the generic regression >> function. >> eg:- if the dataset is of the form, y ~ Sin(x), then we need >> to create a new variable x1 = sin(x) so that we can fit the linear >> regression equation y = a + b * x1 >> >> Once again, we don't have to implement anything in the regression >> function, since this adjustment needs to take place before the data set is >> sent to the regression function. >> >> 3. This brings us to the next point and the golden question: how does a >> user identify which functional form the dataset fits? Usually, we need to >> employ some sort of algorithm to fit a non-linear regression equation like >> Gauss-Newton or a graphing mechanism, which unfortunately is out of the >> scope of the CEP. >> >> *Next Steps* >> >> So considering the above findinds, here are the next steps. >> >> 1. Create Samples for the following >> a) Plain old linear regression (simple and multivariate) >> b) Linear regression with dummy variables >> c) Linear regression on non-linear dataset, using siddhi queries to >> transform functional form to linear prior to sending data to regression >> function. >> >> 2. Document the above samples and complete function documentation >> >> 3. If and when we come across datasets that follow certain non-linear >> functional forms, create simple math functions in siddhi to convert data. >> eg:- Sin(x), power(x,n) etc; >> >> Thats the update as of now. Any thoughts? suggestions? >> >> >> Regards, >> Seshika >> > > > > -- > > *S. Suhothayan * > Associate Technical Lead, > *WSO2 Inc. *http://wso2.com > * <http://wso2.com/>* > lean . enterprise . middleware > > > *cell: (+94) 779 756 757 <%28%2B94%29%20779%20756%20757> | blog: > http://suhothayan.blogspot.com/ <http://suhothayan.blogspot.com/> twitter: > http://twitter.com/suhothayan <http://twitter.com/suhothayan> | linked-in: > http://lk.linkedin.com/in/suhothayan <http://lk.linkedin.com/in/suhothayan>* > > -- *S. Suhothayan* Associate Technical Lead, *WSO2 Inc. *http://wso2.com * <http://wso2.com/>* lean . enterprise . middleware *cell: (+94) 779 756 757 | blog: http://suhothayan.blogspot.com/ <http://suhothayan.blogspot.com/>twitter: http://twitter.com/suhothayan <http://twitter.com/suhothayan> | linked-in: http://lk.linkedin.com/in/suhothayan <http://lk.linkedin.com/in/suhothayan>*
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