In order to analyze the data in presence of seasonality, I used two methods: Proportional hazard model (Cox model) and time series method (Triple Exponential Smoothing (Holt Winters Method)). Now , my question is that what is the difference between them? How should I know which of them is better for my application?
1 Answer
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Probably neither. You should use your actual data to identify an appropriate model. If you just have a single series you might investigate ARIMA modelling and Intervention Detection procedures. If you have causal series then investigate Transfer Functions/Dynamic Regression and Intervention Detection procedures.
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$\begingroup$ Thank you for your answer. I used Holt Winters Method because I saw in some references that if we have "seasonality" AND "trend" in our data we cannot use moving average or single smoothing methods...May I know why do you think those two methods are are not useful for my application? $\endgroup$– FeraCommented Jul 8, 2015 at 0:09
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$\begingroup$ I am not familiar with promotional hazard BUT I am very familiar with the shortcomings of assuming any particular structure for a model as parameter estimates are based upon a particular model. Let the data suggest the model.. To prove this point take your data and estimate the hw model and the errors from the model . Present the normal summary statistics e.g. variance /r-sq /aic and your forecasts. I will take your data and submit it to a commercial package AUTOBOX and present the results... Then you decide which approach is best for the data. $\endgroup$ Commented Jul 8, 2015 at 1:21
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$\begingroup$ Thank you for your suggestion. Regarding ARIMA model, since I am not familiar with that, I have problem with determining p,d and q values for that. Could you briefly help me with that? $\endgroup$– FeraCommented Jul 13, 2015 at 22:44
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$\begingroup$ you might want to look at autobox.com/cms/index.php/blog .There is a discussion and some flow charts that might help. $\endgroup$ Commented Jul 14, 2015 at 1:45