TSO Foundation Model — v11
Time is geometry. The Time-Series Operator (TSO) is a foundation model that learns the shape of dynamical systems instead of token-chunking numbers. Pretraining uses four self-supervised pretexts — reconstruction, Koopman-linear dynamics, scale covariance (a renormalization leg), and the arrow of time — on 40 series across 8 domains (electricity grids, meteorology, ECG, finance, epidemiology, economics, solar physics, chaotic systems). Transfer to a never-seen series is a closed-form Koopman fit on the frozen latent: zero gradient steps.
Results (v11, GPU-pretrained, latent 256 / hidden 768,
25,000 iters × 3 seeds, T4 + AMP)
| Metric | Value |
|---|---|
| Zero-shot wins vs per-series GRU (40 series, in-kernel) | 29/40 (72.5%) |
| Median frozen skill vs persistence | +0.4% (GRU: -60.6%) |
| Solar-cycle rediscovery (held-out sunspots) | 127 mo vs known 132 (10.6 yr) |
| Arrow-of-time pretext accuracy | 95.1% |
The frozen operator rediscovers the ~11-year Schwabe solar cycle from a scale-space scan of its fitted Koopman eigenvalues on the held-out sunspot series — a purely structural, unsupervised discovery.
Zero-shot usage
import torch
from model import FoundationOperator, zero_shot_forecast
model = FoundationOperator(latent_dim=256, hidden=768)
model.load_state_dict(torch.load("foundation_model.pt", map_location="cpu"))
series = [...] # your 1-D series (any domain, any sampling rate)
res = zero_shot_forecast(model, series)
print(res["skill_pct"], res["corr"]) # skill vs persistence on the test split
No training on your data is needed: the Koopman operator is fitted in closed form on the frozen latent (ridge-regularized), then rolled out with an envelope projection that keeps the trajectory on the observed attractor.
Architecture
- Takens embeddings — raw series → delay-embedding (per-series tau).
- Scale space — fine + coarsened (renormalized) embeddings; the
scale_mapforces scale covariance, so periods like the solar cycle reappear as clean eigenmodes at coarse scales. - Koopman lift — a deep encoder flattens the nonlinear attractor into a
latent where dynamics are approximately linear (
K). - Arrow of time — a conv head classifies forward vs reversed windows.
Honest limitations
- Spike / near-unit-root series (Dogecoin, covid-india) still defeat any closed-form probe; persistence is unbeatable there.
- The frozen linear probe plateaus: pretraining past pretext saturation (≈25k iters at this width) does not buy transfer — capacity and corpus breadth are the levers.
- Pretext losses pay off only above ~15k iterations.
Reproduce
Training protocol: scripts/modal_v11.py (Modal, T4). Corpus:
scripts/build_corpus_modal.py. Full study + manuscript:
output/study/paper/main.pdf in the source repo.
Citation
@misc{tso-v11,
title={Time is geometry: an operator foundation model that learns the shape of dynamical systems},
author={{TSO} Project},
year={2026}
}