metadata
library_name: transformers
pipeline_tag: time-series-forecasting
tags:
- time-series
- synthetic-data
- seq2seq
- retail
- qlora
base_model: amazon/chronos-t5-small
alexgrigoras/sdg_chronos_t5_small_dunnhumby
Synthetic time-series generation checkpoint for the DIF-PI framework.
Model summary
This checkpoint is trained as a seq2seq generator on tokenized retail demand windows. It uses a T5-style encoder-decoder backbone, QLoRA when available, extended time-series special tokens, calendar conditioning, multiple-sample generation, and a seasonality-aware calibration step at inference time.
Intended use
The model is intended for research on synthetic retail demand generation and validation inside the DIF-PI framework. It is not intended for safety-critical or fully autonomous business decisions without human review.
Training setup
- Base model: amazon/chronos-t5-small
- Context length: 140
- Prediction length: 30
- Quantization bins: 4094
- Backend: lora