Database Reference
In-Depth Information
It can be shown by a simple calculation that if all rules have the same
frequency (that is assuming a uniform distribution of all input variables),
the expected increase in the error rate as a result of the change in Period 6
is 9% (1 : 11).
4. A Real-World Case Study
4.1.
Dataset Description
In this section, we apply our change detection method to a real-world time
series dataset. The objectives for the case study are: (1) determine whether
or not our method detects changes, which have occurred during some time
periods; (2) determine whether or not our method produces “false alarms”
while the dataset's characteristics do not change over time significantly.
The dataset has been obtained from a large manufacturing plant in
Israel representing daily production orders of products. From now on this
dataset will be referred as “
Manufacturing
”.
The candidate input attributes are: Catalog number
group (CATGRP) — a discrete categorical variable; Market code group
(MRKTCODE) — a discrete categorical variable; Customer code group
(CUSTOMERGRP) — a discrete categorical variable; Processing duration
(DURATION) — a discrete categorical variable which represents the pro-
cessing times as disjoint intervals of variable size; Time left to operate in
order to meet demand (TIME TO OPERATE)—adiscretecategorical
variable which stands for the amount of time between the starting date of
the production order and its due date. Each value represents a distinct time
interval; Quantity (QUANTITY) — a categorical discrete variable which
describes the quantity of items in a production order. Each value represents
a distinct quantity interval. The target variable indicates whether the order
was delivered on time or not (0 or 1).
The time series database in this case study consists of records of pro-
duction orders accumulated over a period of several months. The 'Manu-
facturing' database was extracted from a continuous production sequence.
Without further knowledge of the process or any other relevant informa-
tion about the nature of change of that process, we may assume that no
significant changes of the operation characteristics are expected over such
a short period of time.
Presentation and Analysis of Results.
Table 9 and Figure 2 describe
the results of applying the IFN algorithm to six consecutive months in the
'Manufacturing' database and using our change detection methodology to
detect significant changes that have occurred during these months.
