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language: en
license: apache-2.0
tags:
- time-series
- forecasting
- chronos
- patch-aliasing
base_model: amazon/chronos-bolt-tiny
---
# Chronos-Bolt Patch-Stride Sweep
Retrained variants of [Chronos-Bolt Tiny](https://huggingface.co/amazon/chronos-bolt-tiny) (~8.7M params) with different **patch size (P)** and **patch stride (S)** configurations, trained from scratch on the full official Chronos pre-training data.
## Purpose
These models support the **structural patch-aliasing** study: investigating how the (P, S) patch geometry of Chronos-Bolt creates frequency-dependent artifacts in time-series forecasting. Each model is trained identically except for the patch geometry, so any downstream difference in aliasing probes is attributable to (P, S).
## Models
| Subfolder | P | S | Overlap | Status |
|---|---|---|---|---|
| `p8-s4-seed42` | 8 | 4 | 0.500 | done |
| `p8-s8-seed42` | 8 | 8 | 0.000 | done |
| `p16-s4-seed42` | 16 | 4 | 0.750 | done |
| `p16-s8-seed42` | 16 | 8 | 0.500 | done |
| `p16-s12-seed42` | 16 | 12 | 0.250 | done |
| `p16-s15-seed42` | 16 | 15 | 0.062 | done |
| `p16-s16-seed42` | 16 | 16 | 0.000 | done |
| `p24-s8-seed42` | 24 | 8 | 0.667 | done |
| `p24-s12-seed42` | 24 | 12 | 0.500 | done |
| `p24-s15-seed42` | 24 | 15 | 0.375 | done |
| `p24-s16-seed42` | 24 | 16 | 0.333 | done |
| `p24-s20-seed42` | 24 | 20 | 0.167 | done |
| `p24-s24-seed42` | 24 | 24 | 0.000 | done |
| `p32-s8-seed42` | 32 | 8 | 0.750 | done |
| `p32-s12-seed42` | 32 | 12 | 0.625 | done |
| `p32-s15-seed42` | 32 | 15 | 0.531 | done |
| `p32-s16-seed42` | 32 | 16 | 0.500 | done |
| `p32-s20-seed42` | 32 | 20 | 0.375 | done |
| `p32-s24-seed42` | 32 | 24 | 0.250 | done |
| `p32-s28-seed42` | 32 | 28 | 0.125 | done |
| `p32-s32-seed42` | 32 | 32 | 0.000 | done |
## Training setup
All models share the same training regime (only P and S vary):
- **Architecture**: Chronos-Bolt Tiny (T5-based, ~8.7M params), random initialization
- **Data**: Official Chronos pre-training corpus — TSMixup (10M series) + KernelSynth (1M series) at 9:1 ratio
- **Steps**: 100,000 (fixed budget across all runs)
- **Optimizer**: AdamW, lr=1e-3, linear decay, no warmup
- **Batch size**: 32
- **Precision**: fp32 + TF32 matmuls (Ampere+ GPUs)
- **Context**: 2048 tokens, prediction horizon: 64
- **Quantiles**: 9 (0.1 to 0.9)
- **Seed**: 42
Full provenance is recorded in each subfolder's `run_config.json`.
## Usage
```python
from chronos import BaseChronosPipeline
pipe = BaseChronosPipeline.from_pretrained(
"federicosabbadini/chronos-bolt-patch-sweep",
subfolder="p24-s16-seed42",
device_map="cpu",
)
```
## Files per model
- `config.json` — model architecture config
- `model.safetensors` — trained weights (~34 MB)
- `run_config.json` — full training provenance + result metrics
- `loss_history.npy` — per-step training loss
- `loss_curve.png` — training loss plot
## License
The models are derived from the Chronos architecture (Apache-2.0) and trained on the official Chronos datasets. See the original [Chronos repository](https://github.com/amazon-science/chronos-forecasting) for details.
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