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I assume when you say "trend" you mean long-run stocastic trend. The only way I know of extracting long-run stocastic trend information is through cointegration techniques. Moving average or ARIMA models will not do it. However, you need a lot of observations to make identification of cointegrated time series meaningful. The data can be analyzed in an unrestricted VAR model and then tested. The CATS for RATS program will do this, as will several other econometric packages. Good luck. -jr
Rubin, Thank You for all your suggestion and time. I try those models and contact you for any further advice.
Biswajit, the data is about consumer transaction variable collected for every month for 12 months period. We have thousands of consumers. We need to see whether there are common patterns in consumer spending by clustering techniques.
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Thanks for your suggestion Goldrick. I used Correspondence Analysis as you mentioned, but I'm not sure how good to use this for mining large database.Could you please give me some example or applications on time series data.
Sorry Goldrick, I was working on some other project and couldn't able to respond. I tried doing hierarchical clustering on eigen vectors since we have lot of consumer data. Our results shows that there are peak transactions almost in every month. Should validate these results looking into other parameters.
i will suggest u about this problem, just make clusters of your customers then u can simply make a 3d histogram taking cluster number on one axis and month on the other axis(any software will do this for u).
this may give u a picture of which spending patterns of group of customers in different months
Did you mention what platform you are on? Microsoft's Sequence Clustering algorithm in SQL Server Analysis Services is designed to address that kind of a problem--sequence patterns. Also, I would think that web analytics software might have something to offer as sequences of clicks on websites are critical, but I have very limited experience with that.