ARIMA

ARIMA using For

Example 1

Summary: Generates a forecasted value for 'Steel Shipments' based on ARIMA analysis, using the Match function to handle different cases and populate a list with relevant data.

Code:

dt = Open("data_table.jmp");
y = "";
xlist = {"1", "2", "3", "4"};
testVar = {};
For( i = 1, i <= N Items( xlist ), i++,
    If( i == 3,
        y = Column( "Steel Shipments" )
    );
    Match( xlist[i],
        xlist[1], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[2], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[3],
            Insert Into(
                testVar,
                Eval List(
                    {i, y, Round(
                        ARIMA Forecast(
                            y,
                            96,
                            ARIMA( 1, 0, 1 ),
                            {AR Coefficients( {0.900397691783565} ), MA Coefficients( {0.483316746530245} ), Intercept( 6466.03264802329 )},
                            1,
                            2
                        ),
                        3
                    )}
                )
            ),
        xlist[4], Insert Into( testVar, Eval List( {i, y} ) ),
        Insert Into( testVar, Eval List( {i, y, "error"} ) )
    );
);

Code Explanation:

1. Open data table.
2. Initialize variable y as empty string.
3. Define list xlist with four elements.
4. Initialize empty list testVar.
5. Loop through each item in xlist.
6. If index i equals 3, assign column "Steel Shipments" to y.
7. Use Match to handle different cases based on xlist[i].
8. For first two cases, insert {i, y} into testVar.
9. For third case, insert {i, y, forecast value} into testVar.
10. For fourth case, insert {i, y} into testVar.

Example 2

Summary: Generates a forecasted distribution analysis for continuous variables in a specified data table using ARIMA forecasting and MatchMZ matching.

Code:

dt = Open("data_table.jmp");
y = "";
xlist = {"1", "2", "3", "4"};
testVar = {};
For( i = 1, i <= N Items( xlist ), i++,
    If( i == 3,
        y = Column( "Steel Shipments" )
    );
    MatchMZ( xlist[i],
        xlist[1], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[2], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[3],
            Insert Into(
                testVar,
                Eval List(
                    {i, y, Round(
                        ARIMA Forecast(
                            y,
                            96,
                            ARIMA( 1, 0, 1 ),
                            {AR Coefficients( {0.900397691783565} ), MA Coefficients( {0.483316746530245} ), Intercept( 6466.03264802329 )},
                            1,
                            2
                        ),
                        3
                    )}
                )
            ),
        xlist[4], Insert Into( testVar, Eval List( {i, y} ) ),
        Insert Into( testVar, Eval List( {i, y, "error"} ) )
    );
);

Code Explanation:

1. Open data table.
2. Initialize variable y.
3. Define list xlist.
4. Initialize empty list testVar.
5. Loop through xlist items.
6. Set y for third item.
7. Match current xlist item.
8. Insert i and y for first two items.
9. Insert i, y, and ARIMA forecast for third item.
10. Insert i and y for fourth item.

Example 3

Summary: Generates a distribution analysis for continuous variables in a specified data table using the Distribution platform, incorporating ARIMA forecasting and error handling.

Code:

dt = Open("data_table.jmp");
y = "";
xlist = {"1", "2", "3", "4"};
testVar = {};
For( i = 1, i <= N Items( xlist ), i++,
    If( i == 3,
        y = Column( "Steel Shipments" )
    );
    MatchV3( xlist[i],
        xlist[1], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[2], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[3],
            Insert Into(
                testVar,
                Eval List(
                    {i, y, Round(
                        ARIMA Forecast(
                            y,
                            96,
                            ARIMA( 1, 0, 1 ),
                            {AR Coefficients( {0.900397691783565} ), MA Coefficients( {0.483316746530245} ), Intercept( 6466.03264802329 )},
                            1,
                            2
                        ),
                        3
                    )}
                )
            ),
        xlist[4], Insert Into( testVar, Eval List( {i, y} ) ),
        Insert Into( testVar, Eval List( {i, y, "error"} ) )
    );
);

Code Explanation:

1. Open data table.
2. Initialize variables.
3. Loop through list items.
4. Check if index is 3.
5. Assign column to variable.
6. Match list item 1.
7. Insert into testVar.
8. Match list item 2.
9. Insert into testVar.
10. Match list item 3.
11. Perform ARIMA forecast.
12. Round forecast value.
13. Insert into testVar.
14. Match list item 4.
15. Insert into testVar.
16. Handle unmatched cases.