MDPI, Basel, Switzerland Machine Learning and Knowledge Extraction volume: 8 number: 4 startpage: 90 Fine-Tuned Nonlinear Autoregressive Recurrent Neural Network Model for Dam Displacement Time Series Prediction Dam monitoring data are nonlinear and nonstationary time series. Most existing data-driven dam displacement models are developed independently for each measuring point, disregarding the fact that a dam is a complex structure composed of various interconnected elements that form a unified whole. Regardless of the dam type, all points on the dam are exposed to the same external environmental influences. To account for the correlation between displacement time series at different points, this paper proposes a novel finetuned deep-learning nonlinear autoregressive (NAR) model based on a Long Short-Term Memory (LSTM) network for predicting dam tangential displacement, and a new method for generating source data to train the base model. The models for three measuring points were developed and tested on experimental data collected over a period of slightly more than twelve years. Compared with the model without fine-tuning, the proposed approach achieves an average mean square error (MSE) reduction of 80.68% on the training set and 65.79% on the test set, as well as an average mean absolute error (MAE) reduction of 51.05% and 52.62%, respectively. Furthermore, the proposed model outperforms Random Forest (RF), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) models for dam displacement prediction. info:eu-repo/semantics/article eng application/pdf 2710597 bytes http://creativecommons.org/licenses/by/4.0/legalcode 2026 NAR-LSTM, transfer learning, STL decomposition, DTW, time series prediction, dam displacemen https://unilib.phaidrabg.rs/o:9614 doi:10.3390/make8040090 ISSN: 2504-4990 Ćirović, Vukašin Ranković, Vesna Milivojević, Nikola Milivojević, Vladimir Majkić-Dursun, Brankica