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The results are compared with one of the standout machine learning algorithms, namely eXtreme Gradient Boosting (XGB). Apart from other EPF solutions, in this paper, we focus on the interval before and after the pandemic and the conflict in Ukraine. Furthermore, compared to the previous papers that mainly approached German, Austrian, Australian, Spanish, Nordic electricity Day Ahead Markets (DAM), we emphasize on the EPF for one of the East-European countries\u2014Romania whose market rules closely align with the rules of the European Union electricity DAM. The contribution of this study consists in creating a data set that spans from January 2019 to August 2022 and providing an algorithm to identify the best stacked LSTM architecture to cope with a challenging short-term EPF. The proposed algorithm identifies the most relevant variables using a correlation threshold and performs a combination of three parameters\u2014hidden layer size, dropout and learning rate generating the best EPF results.<\/jats:p>","DOI":"10.1007\/s44196-023-00309-3","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T12:01:26Z","timestamp":1691582486000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Forecasting the Spot Market Electricity Price with a Long Short-Term Memory Model Architecture in a Disruptive Economic and Geopolitical Context"],"prefix":"10.1007","volume":"16","author":[{"given":"Adela","family":"B\u00e2ra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simona-Vasilica","family":"Oprea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandru-Costin","family":"B\u0103roiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"309_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.118049","author":"S Beltr\u00e1n","year":"2022","unstructured":"Beltr\u00e1n, S., Castro, A., Irizar, I., Naveran, G., Yeregui, I.: Framework for collaborative intelligence in forecasting day-ahead electricity price. 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