| # Retail World Model |
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| A transformer-based world model for retail sales prediction, trained on the Favorita store sales dataset. |
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| ## Architecture |
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| - **Encoder**: T5 Encoder processes historical retail time-series |
| - **Latent Dynamics**: LSTM-based world model that imagines future states |
| - **Decoder**: Predicts future sales with probabilistic forecasting (mean + variance) |
| - **Fine-tuning**: LoRA adapters on encoder attention layers |
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| ## Features |
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| - **Context Window**: 60 days of historical sales data |
| - **Prediction Horizon**: 14 days of future sales forecasts |
| - **Inputs**: Sales history, promotion flags, day-of-week, month, product family |
| - **Output**: Probabilistic predictions with 90% confidence intervals |
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| ## Usage |
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| ### Training |
| ```bash |
| python train_v4.py |
| ``` |
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| ### Inference |
| ```bash |
| python inference.py --checkpoint /path/to/model --history history.csv --output predictions.csv |
| ``` |
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| ## Dataset |
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| - **Source**: [t4tiana/store-sales-time-series-forecasting](https://huggingface.co/datasets/t4tiana/store-sales-time-series-forecasting) |
| - **Stores**: 54 Favorita stores in Ecuador |
| - **Product Families**: 33 product categories |
| - **Records**: ~3M daily sales records (2013-2017) |
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| ## Model |
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| - **Base**: T5 Encoder (tiny) |
| - **Parameters**: ~30M base + LoRA adapters |
| - **Loss**: Negative log-likelihood (probabilistic forecasting) |
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| ## References |
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| - Chronos-2: Universal Time Series Forecasting (Amazon Science) |
| - iTransformer: Inverted Transformers for Time Series (NeurIPS 2023) |
| - TabFormer: Tabular Transformers for Sequential Data (IBM Research) |
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