How to use INVOKE function with INT parameter types

Can you tell me how to use invoke function with int parameter type ?

Pass the int as an Integer.

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    select
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    (PURCHASE_WEEK, WEEK, SALES)
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    (PURCHASE_WEEK, WEEK, SALES)
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    (PURCHASE_WEEK, WEEK, SALES)
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    There are a lot of linear regression models and approaches for sales forecast out on the market, however I will focus on what oracle 11g offers i.e. package SYS.DBMS_DATA_MINING to create a model using regression as mining function and then, once the model is created, to apply prediction function on the model.
    Therefore I'll have to go through few steps:
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    CREATE OR REPLACE VIEW t_sales_norm AS
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    sales,
    (sales - 91423.95)/27238.3693126778 sales_norm
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    whereas the numerical values are the mean and the standard deviation:
    select avg(sales) from t_sales;
    91423.95
    select stddev(sales) from t_sales;
    27238.3693126778
    ii) auto-correlation. For the sake of simplicity, I will safely assume that there is no auto-correlation (no repetitive pattern in sales among the weeks). Therefore to define the lag data I will consider the whole set:
    CREATE OR REPLACE VIEW t_sales_lag AS
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    sales,
    LAG(sales_norm, 1) OVER (ORDER BY week) L1,
    LAG(sales_norm, 2) OVER (ORDER BY week) L2,
    LAG(sales_norm, 3) OVER (ORDER BY week) L3,
    LAG(sales_norm, 4) OVER (ORDER BY week) L4,
    LAG(sales_norm, 5) OVER (ORDER BY week) L5,
    LAG(sales_norm, 6) OVER (ORDER BY week) L6,
    LAG(sales_norm, 7) OVER (ORDER BY week) L7,
    LAG(sales_norm, 8) OVER (ORDER BY week) L8,
    LAG(sales_norm, 9) OVER (ORDER BY week) L9,
    LAG(sales_norm, 10) OVER (ORDER BY week) L10,
    LAG(sales_norm, 11) OVER (ORDER BY week) L11,
    LAG(sales_norm, 12) OVER (ORDER BY week) L12,
    LAG(sales_norm, 13) OVER (ORDER BY week) L13,
    LAG(sales_norm, 14) OVER (ORDER BY week) L14,
    LAG(sales_norm, 15) OVER (ORDER BY week) L15,
    LAG(sales_norm, 16) OVER (ORDER BY week) L16,
    LAG(sales_norm, 17) OVER (ORDER BY week) L17
    FROM t_sales_norm) a;
    iii) choosing the training data. Again, I will choose the whole set of 17 weeks, as for this discussion in not relevant how big should be the set of training data.
    CREATE OR REPLACE VIEW t_sales_train AS
    SELECT week, sales,
    L1, L2, L3, L4, L5, L6, L7, L8, L9, L10,
    L11, L12, L13, L14, L15, L16, L17
    FROM t_sales_lag a
    WHERE week >= 1 AND week <= 17;
    iv) build the model
    -- exec SYS.DBMS_DATA_MINING.DROP_MODEL('t_SVM');
    BEGIN
    sys.DBMS_DATA_MINING.CREATE_MODEL( model_name => 't_SVM',
    mining_function => dbms_data_mining.regression,
    data_table_name => 't_sales_train',
    case_id_column_name => 'week',
    target_column_name => 'sales');
    END;
    v) finally, where I am confused is applying the prediction function against this model and making sense of the results.
    On a search on Google I found 2 ways of applying this function to my case.
    One way is the following:
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    LAG(sales,1) OVER (ORDER BY week) as l1,
    LAG(sales,2) OVER (ORDER BY week) as l2,
    LAG(sales,3) OVER (ORDER BY week) as l3,
    LAG(sales,4) OVER (ORDER BY week) as l4,
    LAG(sales,5) OVER (ORDER BY week) as l5,
    LAG(sales,6) OVER (ORDER BY week) as l6,
    LAG(sales,7) OVER (ORDER BY week) as l7,
    LAG(sales,8) OVER (ORDER BY week) as l8,
    LAG(sales,9) OVER (ORDER BY week) as l9,
    LAG(sales,10) OVER (ORDER BY week) as l10,
    LAG(sales,11) OVER (ORDER BY week) as l11,
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    LAG(sales,14) OVER (ORDER BY week) as l14,
    LAG(sales,15) OVER (ORDER BY week) as l15,
    LAG(sales,16) OVER (ORDER BY week) as l16,
    LAG(sales,17) OVER (ORDER BY week) as l17
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    1, 55488, 68861.084076412
    2, 78336, 104816.995823913
    3, 77248, 104816.995823913
    4, 106624, 104816.995823913
    As you can see for the first row there is a value of 68861.084 and for the rest of 16 values is always one and the same 104816.995.
    Question: where is my week 18 prediction ? or maybe I should say which one is it ?
    Another way of using prediction even more confusing is against the lag table:
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    2, 78336, 75512.3642096908
    3, 77248, 85711.5003385927
    4, 106624, 98160.5009687461
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    Thank you very much for all help that you can provide on this matter.
    It is as always highly appreciated.
    Serge F.

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