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Such a generalization is motivated by the difficulty of forecasting very long time series efficiently. The LSTCN model can be defined as a collection of STCN blocks, each processing a specific time patch of the (multivariate) time series being modeled. In this neural ensemble, each block passes information to the subsequent one in the form of weight matrices representing the prior knowledge. As a second contribution, we propose a deterministic learning algorithm to compute the learnable weights while preserving the prior knowledge resulting from previous learning processes. As a third contribution, we introduce a feature influence score as a proxy to explain the forecasting process in multivariate time series. The simulations using three case studies show that our neural system reports small forecasting errors while being significantly faster than state-of-the-art recurrent models.<\/jats:p>","DOI":"10.1007\/s00521-022-07348-5","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T12:03:58Z","timestamp":1653393838000},"page":"16959-16971","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Long short-term cognitive networks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-3701","authenticated-orcid":false,"given":"Gonzalo","family":"N\u00e1poles","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isel","family":"Grau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnieszka","family":"Jastrz\u0119bska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yamisleydi","family":"Salgueiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"issue":"3","key":"7348_CR1","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1007\/s11063-019-10061-5","volume":"50","author":"SA Abdulkarim","year":"2019","unstructured":"Abdulkarim SA, Engelbrecht AP (2019) Time series forecasting using neural networks: are recurrent connections necessary? 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