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In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines.<\/jats:p>","DOI":"10.14778\/3514061.3514067","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T18:26:10Z","timestamp":1655922370000},"page":"1201-1214","source":"Crossref","is-referenced-by-count":1161,"title":["TranAD"],"prefix":"10.14778","volume":"15","author":[{"given":"Shreshth","family":"Tuli","sequence":"first","affiliation":[{"name":"Imperial College London, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuliano","family":"Casale","sequence":"additional","affiliation":[{"name":"Imperial College London, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas R.","family":"Jennings","sequence":"additional","affiliation":[{"name":"Loughborough University, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"An optimized model using LSTM network for demand forecasting. 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