41x Knowledge Distillation from Recurrent Ensembles to Liquid Continuous-Time Networks for Financial Time Series Classification
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| Natura: | Recurso digital |
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2026
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| _version_ | 1866901972472299520 |
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| author | Kilgore, Brian |
| author_facet | Kilgore, Brian |
| contents | We present a knowledge distillation framework that compresses a 137,763-parameter recurrent ensemble (LSTM + GRU) into a 3,328-parameter Liquid Continuous-time Closed-form (LiquidCfC) network, achieving 41.4x parameter reduction for directional classification on forex time series. The teacher achieves a pooled bootstrap Sharpe ratio of 71.4 (95% CI: [58.4, 85.1]) while the student achieves 16.3 (95% CI: [14.76, 17.89]), demonstrating statistically significant predictive fidelity under extreme compression. We introduce a deployment gate criterion (G3) based on KL divergence at unit temperature, providing a principled accept/reject mechanism for compressed model deployment. Validated via Monte Carlo block bootstrap (10,000 resamples) on expanding-window walk-forward cross-validation across four major currency pairs. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20045638 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | 41x Knowledge Distillation from Recurrent Ensembles to Liquid Continuous-Time Networks for Financial Time Series Classification Kilgore, Brian knowledge distillation LiquidCfC continuous-time neural networks model compression financial time series LSTM GRU forex walk-forward validation block bootstrap We present a knowledge distillation framework that compresses a 137,763-parameter recurrent ensemble (LSTM + GRU) into a 3,328-parameter Liquid Continuous-time Closed-form (LiquidCfC) network, achieving 41.4x parameter reduction for directional classification on forex time series. The teacher achieves a pooled bootstrap Sharpe ratio of 71.4 (95% CI: [58.4, 85.1]) while the student achieves 16.3 (95% CI: [14.76, 17.89]), demonstrating statistically significant predictive fidelity under extreme compression. We introduce a deployment gate criterion (G3) based on KL divergence at unit temperature, providing a principled accept/reject mechanism for compressed model deployment. Validated via Monte Carlo block bootstrap (10,000 resamples) on expanding-window walk-forward cross-validation across four major currency pairs. |
| title | 41x Knowledge Distillation from Recurrent Ensembles to Liquid Continuous-Time Networks for Financial Time Series Classification |
| topic | knowledge distillation LiquidCfC continuous-time neural networks model compression financial time series LSTM GRU forex walk-forward validation block bootstrap |
| url | https://doi.org/10.5281/zenodo.20045638 |