Motifs discovery in spatial-time series

Amin Bazaz, Heraldo Borges, Eduardo Ogasawara

2018-08-23

This package STSMotifs allows to perform a research of motif in spatial-time series. The main purpose is to find a way to handle the issue of large amounts of data. The package offers a way to do this research quickly and efficiently. To find the motifs, the Combined Series Approach (CSA) is used. The process is decomposed by several steps :

  1. Generation of candidates
  2. Treatment of candidates
  3. Rank the selected candidates

Input

To use functions of this package, some inputs are needed. The quality of outputs depends strongly by these parameters.

#>      1    2    3     4     5     6    7     8    9   10
#> 1  737 1350  869   750  1138   758 1006  1095   99  -83
#> 2  283  565  504   317  1849   944  -80  -895 -936  906
#> 3 -118 -375 -564  -803   870   472 -922 -1009 -698  741
#> 4 -696 -844 -654 -1303  -474  -591 -262  1034 1012  376
#> 5 -251 -622  -14  -587 -1108 -1401  404  1545 1696  247
#> 6  645  -10   -4   411  -858 -1261 -574  -329 -367 -680

A part of the process is applied into blocks (subsets of the original dataset). With the tslice (“Time slice” number of rows in each block) and sslice (“Space slice” number of columns in each block), the user can specify the block size and the block shape.

Description of each step

Generation of candidates

In this step, using tslice and sslice parameters, we create blocks from the original dataset. By using CSA, each column of the block are combined to create a single big series. The output of this step is described below.

See more at Generation of candidates

Treatment of candidates

This step requires the candidates and the kappa and sigma thresholds. All the information about candidates are extracted and manipulated. In the end, the candidates that passed the restriction of the two thresholds are stored into a list of motifs. Each motif has as information:

See more at Treatment of candidates

Rank the selected candidates

The previous step has created a list of motifs with all information about them. These motifs are ranked by their global and spatial occurrences. The output is the same as the previous step but ordered.

Plot the result

There are three ways to visualize the result:

Example

To see an example of output : Output Example