A modern, parameter-matched retest of periodic recurrent computation inspired by the Clockwork RNN. Four eight-unit hidden blocks update at periods 1, 2, 4, and 8. The model competes with an ordinary tanh RNN and a smaller GRU on noisy three-timescale forecasting.

All variants train on length-64 sequences and are evaluated both at that length and zero-shot at length 256. The interactive Space overlays their predictions on the same generated signal.

All models contain exactly 1,153 parameters. At zero-shot length 256, Clockwork RNN reached 0.0570 RMSE, versus 0.0492 for the ordinary RNN and 0.0466 for the GRU. Periodic hidden updates did not improve this benchmark; the matched controls make that negative result explicit.

This free static Space preserves the complete original Gradio source, trained artifacts, evaluation files, and local launch requirements. Hugging Face now requires PRO for CPU-backed Gradio hosting, so the public landing page is static while the checked-in app.py remains the authoritative runnable demo.