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<h5>Description:</h5>
<p>The Long Short-term Cognitive Network (LSTCN) model [1] is an efficient recurrent neural network for time series forecasting. It supports both one-step-ahead and multiple-step-ahead forecasting of univariate and multivariate time series. The LSTCN model is competitive compared to state-of-the-art recurrent neural networks such as LSTM and GRU in terms of forecasting error while being much faster.</p>

<h5>More Information:</h5>
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<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/nl/onderzoek/onderzoeksuitdagingen/ai-driven-data-science" data-entry-id="58" target="_blank" rel="noopener" data-entry-id="58">GC1</a> - WP4</li>
<li><strong>Responsible Research Lead</strong>: <a href="mailto:inneke.vannieuwenhuyse@uhasselt.be" target="_blank" rel="noopener">Inneke Van Nieuwenhuyse</a></li>
<li><strong>Authors</strong>: <a href="mailto:inneke.vannieuwenhuyse@uhasselt.be" target="_blank" rel="noopener">Inneke Van Nieuwenhuyse</a>, <a href="mailto:alejandro.moraleshernandez@uhasselt.be" target="_blank" rel="noopener">Alejandro Morales Hern&aacute;ndez</a></li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://github.com/gnapoles/lstcn" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong style="color: inherit; font-size: inherit;">Categories:</strong><span style="color: inherit; font-size: inherit;"> Time series</span></li>
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<p>Long Short-term Cognitive Network (LSTCN) model for time series forecasting</p>