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However, RFA relies on only a single, fixed projection for approximation, which does not capture the input distribution and can lead to low efficiency and accuracy, especially on time series data. In this paper, we propose DARKER, an efficient transformer with a novel<jats:bold>DA<\/jats:bold>ta-d<jats:bold>R<\/jats:bold>iven<jats:bold>KER<\/jats:bold>nel-based attention mechanism. To precisely present the technical details, this paper discusses them with a fundamental time series task, namely, time series classification (tsc). First, the main novelty of DARKER lies in approximating the softmax kernel by learning multiple machine learning models with trainable weights as multiple projections offline, moving beyond the limitation of a fixed projection. Second, we propose a projection index (called pIndex) to efficiently search the most suitable projection for the input for training transformer. As a result, the overall time complexity of DARKER is linear with the input length. Third, we propose an indexing technique for efficiently computing the inputs required for transformer training. Finally, we evaluate our method on 14 real-world and 2 synthetic time series datasets. The experiments show that DARKER is 3\u00d7-4\u00d7 faster than vanilla transformer and 1.5\u00d7-3\u00d7 faster than other SOTAs for long sequences. In addition, the accuracy of DARKER is comparable to or higher than that of all compared transformers.<\/jats:p>","DOI":"10.14778\/3681954.3681996","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T16:23:36Z","timestamp":1725035016000},"page":"3229-3242","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DARKER: Efficient Transformer with Data-Driven Attention Mechanism for Time Series"],"prefix":"10.14778","volume":"17","author":[{"given":"Rundong","family":"Zuo","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guozhong","family":"Li","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Cao","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byron","family":"Choi","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sourav S","family":"Bhowmick","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh.","author":"Bagnall Anthony","year":"2018","unstructured":"Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 2018. 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