Sales dataset taken from [2], which comprises 811 product one year sales quantities. We applied this dataset to test the Sequence Detector. The results are available in [1]. The results of the test are various statistics on detected sequences. The testing set of products is re-tested by simultaneously rising the projection threshold, until no more sequences could be detected or max_th parameter is reached.
sales_dataset_test(learning_set = 1:20, testing_set = 21:40,
th_increment = 1, max_th = NULL)(vector) - A set of products to learn ETTs in the Sequence Detector.
(vector) - A set of products to test previously learned sales numbers.
(integer) - A threshold increment between two tests.
(integer) - Maximal thershold for testing. When reached, no further tests and no further threshold increment is done. If NULL, re-testing is done while there are some sequences detected.
A list that comprises sequence statistics for all tests and thresholds.
[1] D. Krle<U+017E>a, B. Vrdoljak, and M. Br<U+010D>i<U+0107>, Latent Process Discovery using Evolving Tokenized Transducer, IEEE Access, vol. 7, pp. 169657 - 169676, Dec. 2019 [2] S. C. Tan and J. P. San Lau, Time series clustering: A superior alternative for market basket analysis, in Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), Singapore, 2014, pp. 241<U+2013>248.