TS_Init), all results entries in the resulting
HCTSA.matare set to
NaN, corresponding to results that are as yet uncomputed.
TS_Compute, which stores results back into the matrices in
HCTSA.mat. This function can be run without inputs to compute all missing values in the default hctsa file,
TS_Computewill begin evaluating operations on time series in
HCTSA.matfor which elements in
NaN(i.e., computations that have not been run previously). Results are stored back in the matrices of
TS_DataMat(output of each operation on each time series),
TS_CalcTime(calculation time for each operation on each time series), and
TS_Quality(labels indicating errors or special-valued outputs).
ts_id) and operation IDs (
op_id). This can be achieved by setting the second and third inputs:
HCTSA.matis the default):
TS_Compute(false)) to results using a 16-core machine, with parallelization enabled (e.g.,
INP_ops_reduced.txt. We plan to reduced additional reduced feature sets, determined according to different criteria, in future.
TS_Computecommands are run in a loop over time series, compute small sections of the dataset at a time (and then saving the results to file, e.g.,
HCTSA.mat), eventually covering the full dataset iteratively.
HCTSA.mat) can be split into smaller pieces using
TS_Subset, which outputs a new data file for a particular subset of your data, e.g.,
TS_Subset('raw',1:100)will generate a new file,
HCTSA_subset.matthat contains just time series with IDs from 1 to 100. Computing features for time series in each such subset can then be run on a distributed computing setup. For example, with a different compute node computing a different subset (by queuing batch jobs that each work on a given subset of time series). After all subsets have been computed, the results are recombined into a single