Multiple Timeseries
A timeseries represents an ordered collection of values of an event (or item) over time. A multiple timeseries represents the collection of multiple timeseries gathered from multiple items over a particular duration. Depending on the values stored in a series, a multiple timeseries can be broadly classified into two types:
Binary multiple timeseries and
(non-binary) multiple timeseries .
Binary Multiple Timeseries
A binary multiple time series represents the binary data of multiple items split into temporal windows. An example of this series is shown below.
windowID
binary sequences
1
(a,1) (a,3) (b,2) (b,3) (c,2) (c,3)
2
(a,1) (b,1) (b,2) (b,3) (c,1)
3
(a,1) (a,2) (b,1) (b,3) (c,2)
4
(a,1) (b,1) (b,2) (c,3)
5
(a,1) (a,3) (b,3) (c,2) (c,2)
6
(a,1) (a,2) (b,2) (b,3)
Rules to create a binary multiple time series.
First column must contain an integer representing an windowID.
Remaining columns must contain items and their timestamps within braces.
In the braces, starting from left hand side, the first word/letter represents an item and the other word/letter represents an timestamp.
Columns are seperated with a seperator.
‘ Tab space ’ is the default seperator. However, transactional databases can be constructed using other seperators, such as comma and space.
Format of a binary multiple time series:
>>> windowID<sep>(item,timestamp)<sep>(item,timestamp)<sep>...<sep>(item, timestamp)
An example
1
(a,1) (a,3) (b,2) (b,3) (c,2) (c,3)
2
(a,1) (b,1) (b,2) (b,3) (c,1)
3
(a,1) (a,2) (b,1) (b,3) (c,2)
4
(a,1) (b,1) (b,2) (c,3)
5
(a,1) (a,3) (b,3) (c,2) (c,2)
6
(a,1) (a,2) (b,2) (b,3)