Synthetic training data for a small time-series forecaster: stuck at ~0.2% gains. What are we missing? [D]
A small transformer forecaster starts from a fixed pretrained checkpoint and gets a short, fixed training budget on series produced by our synthetic data generator.
Proof1 community thread
Key points
- Setup (the data is our only lever)
- With a warm-started small forecaster and a tiny budget, which data-side changes have moved error by more than ~1% for you?
- Are there synthetic priors for TS foundation models, beyond KernelSynth / ForecastPFN-style generators, where the gains survive distribution shift?
- How would you quickly find which slices of a rolling eval set a reference model is weak on?
Sources (1)
- [1]Synthetic training data for a small time-series forecaster: stuck at ~0.2% gains. What are we missing? [D]r/MachineLearning (top, daily) · Oct 14, 05:05 PM
A small transformer forecaster starts from a fixed pretrained checkpoint and gets a short, fixed training budget on series produced by our synthetic data generator.
Setup (the data is our only lever)
Extractive summary: sentences quoted from the sources.
Before this
- Oct 14, 2026mistralai/Timestral-209M
- Oct 14, 2026Train LoRA directly on Quantized GGUFs, no PyTorch, no Parent(FP16) needed, just your GGUF and the data
- Oct 13, 2026Dyna-2: General Purpose Physical Agents with Hour-Long Autonomy — Jason Ma, Dyna Robotics
- Oct 13, 2026Run fast, private agents on-device with LFM2.5-24B-A2B
- Oct 13, 2026I built a pytorch library of pretrained models for 3D point clouds following timm standards [P]
- Oct 13, 2026black-forest-labs/flux-3-image-klein-comfy
