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September 10, 2025International Journal of Mathematics Statistics and Computing

Comparison of Activation Functions in Recurrent Neural Network for Litecoin Cryptocurrency Price Prediction

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Authors

MSMoch Panji Agung SaputraPadjadjaran UniversityAAAstrid Sulistya AzahraPadjadjaran UniversityDPDede Irman PirdausUniversitas Bale Bandung

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Implication

This analysis reveals swish as the top activation function for predicting litecoin prices, suggesting improved investment strategies.

Key Points

  • Swish activation function demonstrates superior price prediction accuracy in litecoin forecasting.
  • Results indicate swish achieved the lowest RMSE of 4.58 and the highest R² score of 0.9578.
  • Historical data from May 2020 to April 2025 was analyzed using various activation functions in RNN.
  • This study highlights the importance of effective activation functions in cryptocurrency price prediction models.

Cite This Study

Saputra et al. (2025) studied this question.

synapsesocial.com/papers/68c1aad354b1d3bfb60e39fbhttps://doi.org/10.46336/ijmsc.v3i3.233
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Litecoin price prediction based on random forest regression, LightGBM and LSTM2024
  2. 2Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices2024 · 2 citations
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  4. 4Analisis Kinerja Algoritma Machine Learning dalam Prediksi Harga Cryptocurrency2024
  5. 5Evaluations of the machine learning schemes for cryptocurrency prediction2024 · 1 citations