I would think it should live in Siddhi.

Paul


On 23 April 2014 11:27, Sriskandarajah Suhothayan <[email protected]> wrote:

> 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 <%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>*
>
>
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>


-- 
Paul Fremantle
CTO and Co-Founder, WSO2
OASIS WS-RX TC Co-chair, Apache Member

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