Apply changes from PR #393 by @MarcoGorworworelli:
- Normalize covariates per-input instead of batch-wide to prevent
each input's result from depending on batch composition
- Fit separate ridge regressions per time series instead of a single
batched regression, preventing cross-series data leakage
- Applied to both src/timesfm/utils/xreg_lib.py and v1/src/timesfm/xreg_lib.py
Apply changes from PR #396 by @shahrukhx01:
- Fix typo 'complied' -> 'compiled' in ForecastConfig docstrings
- Replace bare print() with logging.info() in load_checkpoint()
PEFTTrainer with production-grade training loop:
- PyTorch DDP multi-GPU via torchrun
- Mixed-precision training (fp16/bf16) with GradScaler
- Gradient checkpointing for long contexts
- Cosine-with-warmup LR schedule
- MSE loss + optional pinball quantile loss (9 channels)
- Early stopping on validation loss
- Adapter-only checkpointing (safetensors)
- W&B logging (rank-0 only)
- Differentiable training forward that replicates the 2.5
patch -> RevIN -> transformer -> output-head -> un-RevIN path
Sliding-window dataset that produces (context, mask, target) tuples:
- Accepts list of arrays, long-format, or wide-format DataFrames
- Context length auto-rounded to multiple of patch_len (32)
- Left-pads short series with proper masking
- Configurable stride for window overlap
darkpowerxo