Prediction for next orders, any ideas?

EJECTY

Dear Community!

 

I have a .csv file with 100.000 rows and 439 columns. This spreadsheet represents the customers' habits for using a specific service. For each rows there is an ID for every customer and every transaction date with the following format: 1 for Monday, 2 for Tuesday... etc. I need to predict the next date of transaction for every customer, using these past records.

 

Here's an example for the format of the database:

customer_id                      transaction1 transaction2 ... transaction438
1                                       1 2 3 4 5 6 7 ... 745 746 747
2                                       2 7 16 20 21 23 28 ... 412
3                                       1 2 3 4 5 6 7 ... 285 322
4                                       5 7 8 12 14 19 21 ... 924 925 926

 

Any ideas what model should I use for this prediction for the best accuracy?

 

NOTE: The database have lots of missing values depends on the frequency of ordering.

Thomas_Ott

This looks like some sort of sales projection analysis. I would look at the process I shared here: http://community.rapidminer.com/t5/RapidMiner-Studio/How-to-get-forecast-values-of-future-from-time-series-data/m-p/37698

 

You would need to do a bit of missing value replacements using the Replace Missing Values operator and need to install the Series extension from our marketplace.  Is there seasonality involved?