# Stats Geeks – what method would you use?

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Viewing 13 posts - 1 through 13 (of 13 total)
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• #184880

Eugene Byelyakov
Participant

Firstly, you might want to ensure your data is normally distributed and in control — otherwise predictions would be useless. Depending on the type of data you could be looking in using Xbar, NP, P and C charts.Secondly, predicting, to me, sounds as determining an equation that will show correlation between response variable and predictor(s). This is achieved by using regression analysis.

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#184882

Severino
Participant

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#52539

SiggySig
Member

I’ve got a set of time-series binary data I’d like to discover patterns in, and hopefully be able to develop a predictive model for – not sure what’s the best approach to analyzing, looking for some suggestions.

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#184889

Taylor
Participant

Siggysig
I found some interesting stuff on Google by doing a quick search of “Predicting Binary Data” may be of some help. Way over my head, but the premise seemed to be on the same line of what you are looking for

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#184890

JB
Participant

I’m not exactly sure what you’re trying to model, but binary logistic regression may have some potential use.  See the following example with binary output:
http://europe.isixsigma.com/library/content/c070418b.asp
JB

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#184928

Jonathon Andell
Participant

Please tell us a little more. What is the process, and what outcomes do the data represent?

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#184940

SiggySig
Member

Not a process per se – the binary is based on spot market electricity prices.
Basically I have the forward price, established the day before, and the real-time price, established in the trading day. I’m trying to determine how best to predict whether the forward price will be higher or lower than the real time price.

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#184953

Participant

Why don’t you see, first, what you have when you align the forward price and the realtime price on the projected/real date? Then you could use a 2nd y-axis to run the difference. Might want to include some consideration for factors that might have affected both the projected and the actual prices. Sounds like a fun analysis! (Why would the type of data affect the analysis? I’m not getting that part.)

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#184954

Cinnamond
Participant

You do not have to use binary data.   If you have the before and after values, then do all your work on the difference.   This should be continuous and if done correctly will be a more powerful analysis.   Good luck

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#184960

BTDT
Participant

SiggySig:Your data is not binary(high/low) it is continuous (\$/KWhr)General advice?- Logarithmically transform your price data- Include daily average system wide load for California prices- Ignore ambient temperature- Capture weekly variation using the autoregressive structure of your models- Use dummy variables for holidays and long weekends- Consider using a regime switching or mean reverting jump diffusion models to handle spikes- Read this paper: http://mpra.ub.uni-muenchen.de/10428/Cheers, Alastair

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#184964

Severino
Participant

So the lesson learned here is when you don’t present enough information about your goal and what data you have at your disposal you will wander about with obscure answers for days, but when you give the right info you’ll get a meaningful response within about a day.
Great information Alastair! 5 stars.

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#184974

Kluttz
Member

The answer you’re looking for isnt a traditional Six Sigma tool.  Stochastic modeling / Monte Carlo simulation is the correct method for your problem.  Any other tool mentioned here will be static as opposed to probabilistic.
That will be twelve thousand dollars please.

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#184978

Jonathon Andell
Participant

The data is only binary if you convert perfectly good continuous data into vastly inferior discrete (“binary”) data. You could consider some time series models, or perhaps some regression analysis if you also have potential X-variables (don’t forget higher0order terms and “lag” functions). Either way, never make the mistake of degrading continuous data into discrete.

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