CHANGE DETECTION IN CLASSIFICATION MODELS INDUCED FROM TIME SERIES DATA - Data Mining in Time Series Databases

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The XP statistics, as described in Table 9 and Figure 2, refer only to

the target variable (delivery on time). The magnitude of change in the can-

didate input variables as evaluated across the monthly intervals is shown

in Table 10.

Table 9. Results of the CD hypothesis testing on the 'Manufacturing' database

Month

CD

XP

e M K− 1 ,K

e M K− 1 ,K − 1

d (95%)

1 − p -value

1

—

2

14.10%

12.10%

2.00%

4.80%

58.50%

78.30%

3

11.70%

10.40%

1.30%

3.40%

54.40%

98.80%

4

10.60%

9.10%

1.50%

2.90%

68.60%

76.50%

5

11.90%

10.10%

1.80%

2.80%

78.90%

100.00%

6

6.60%

8.90%

2.30%

95.00%

63.10%

99%

100%

95%

78%

76%

80%

79%

63%

69%

60%

58%

54%

40%

20%

0%

2

3

4

5

6

month

CD

XP

Fig. 2. Summary of implementing the change detection methodology on 'Manufactur-

ing' database (1 − p -value).

Table 10. XP confidence level of all independent and dependent variables

in 'Manufacturing' database (1 − p -value).

CAT MRKT Duration

Time to Quantity

Customer

GRP

Code

operate

GRP

Domain

18

19

15

18

Month 2

100%

Month 3

100%

Month 4

100%

99.8%

100%

Month 5

100%

99.9%

100%

Month 6

100%

Data Mining in Time Series Databases

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