6:40 PM - 7:00 PM
[1I4-J-2-05] Flexible segmentation for multi-dimensional time series data
Keywords:time series, segmentation
Along with the development of IoT technology, large amount of time series data are becoming available in recent
years. To discover useful knowledge from time series data, the method of segmenting multivariate time series data
into characteristic patterns has been receiving much attention. However, positions of segmentation obtained by
previous methods are identical across variables, which makes difficult to capture the specific feature of each variable.
To deal with this problem, we propose a new method that can obtain appropriate positions of segmentation for
each variable. Moreover, we experimentally show the effectiveness of our proposed method using both artificial and
real datasets.
years. To discover useful knowledge from time series data, the method of segmenting multivariate time series data
into characteristic patterns has been receiving much attention. However, positions of segmentation obtained by
previous methods are identical across variables, which makes difficult to capture the specific feature of each variable.
To deal with this problem, we propose a new method that can obtain appropriate positions of segmentation for
each variable. Moreover, we experimentally show the effectiveness of our proposed method using both artificial and
real datasets.