{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:38:22Z","timestamp":1783438702573,"version":"3.54.6"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>We demonstrate that CNN deep neural networks can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions. \nThis is important for a number of applications where predictions are the basis for decisions and actions. Hence, confidence in the prediction result is crucial. \nWe design a two stage convolutional neural network architecture which uses particular kernel sizes.\nThis allows us to utilise gradient based techniques for generating saliency maps for both the time dimension and the features.\nThese are then used for explaining which features during which time interval are responsible for a given prediction, as well as explaining during which time intervals was the joint contribution of all features most important for that prediction. \nWe demonstrate our approach for predicting the average energy production of photovoltaic power plants and for explaining these predictions.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/932","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"6488-6490","source":"Crossref","is-referenced-by-count":75,"title":["Explainable Deep Neural Networks for Multivariate Time Series Predictions"],"prefix":"10.24963","author":[{"given":"Roy","family":"Assaf","sequence":"first","affiliation":[{"name":"IBM Research, Zurich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anika","family":"Schumann","sequence":"additional","affiliation":[{"name":"IBM Research, Zurich"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:52:51Z","timestamp":1564300371000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/932"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/932","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}