| Title: | Bio-inspired spiking recurrent networks with evolutionary optimization for non-stationary cryptocurrency forecasting |
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| Authors: | ID Walugembe, Francis Noah (Author)
ID Wielgosz, Maciej (Author)
ID Mertik, Matej (Author)
ID Gams, Matjaž, Institut "Jožef Stefan" (Author) |
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| Files: |  URL - Source URL, visit https://www.mdpi.com/2504-2289/10/7/200
 PDF - Presentation file, download (10,32 MB)
MD5: 6AAA782851404C138BD4DB669D9DAEA5
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | Forecasting cryptocurrency prices remains difficult because market dynamics are highly volatile, non-stationary, and regime-dependent. This study investigates whether combining a spiking-inspired recurrent architecture with the Grey Wolf Optimizer (GWO) can improve one-step-ahead Bitcoin forecasting within a controlled model family. We compare four configurations, LSTM, SLSTM, GWO-LSTM, and GWO-SLSTM, on 4039 daily BTC–USD closing prices from 17 September 2014 to 9 October 2025 using Min–Max normalization, strict chronological splitting, windowed regime-based robustness analysis across three distinct market regimes, and repeated-run testing. The proposed SLSTM replaces the conventional hidden-state recurrence with leaky integrate-and-fire-inspired synaptic, membrane, and adaptive-threshold dynamics, functioning as a spiking-inspired recurrent model with thresholded event gating (reset = `none’, learnable threshold). On the primary hold-out split, GWO-SLSTM achieved a test RMSE of 1840.97 and a test MAPE of 1.76%, compared with 2217.24 and 2.46% for GWO-LSTM, 3501.48 and 3.86% for SLSTM, and 4030.10 and 4.40% for LSTM. Both GWO-optimized models exhibited substantial improvements over their non-optimized counterparts, while the SLSTM baseline also outperformed the plain LSTM, indicating gains from both spiking-inspired recurrence and evolutionary hyperparameter optimization. Both optimized models exhibited near-zero bias (PBIAS 0.11% for GWO-LSTM and 0.36% for GWO-SLSTM). Within the present implementation, GWO-SLSTM also trained faster than GWO-LSTM (39.71 s vs. 137.28 s), although this runtime difference should be interpreted as setup-specific because the model families were implemented in different frameworks and stopped after different numbers of epochs. Overall, within the expanded univariate BTC–USD setting, the results support GWO-SLSTM as a strong within-family candidate for one-step-ahead forecasting under non-stationary conditions. |
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| Keywords: | cryptocurrency, bitcoin, time-series forecasting |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 18.03.2026 |
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| Article acceptance date: | 03.06.2026 |
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| Publication date: | 23.06.2026 |
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| Publisher: | MDPI |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-32 |
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| Numbering: | Vol. 10, iss. 7, [article no.] 200 |
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| Source: | Švica |
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| PID: | 20.500.12556/DiRROS-30786  |
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| UDC: | 004.8 |
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| ISSN on article: | 2504-2289 |
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| DOI: | 10.3390/bdcc10070200 |
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| COBISS.SI-ID: | 283359491 |
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| Copyright: | © 2026 by the authors. |
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| Note: | Nasl. z nasl. zaslona;
Soavtorja iz Slovenije: Matej Mertik, Matjaž Gams;
Opis vira z dne 2. 7. 2026;
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| Publication date in DiRROS: | 03.07.2026 |
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| Views: | 66 |
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| Downloads: | 48 |
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