Time Series Forecasting
Transformers
Safetensors
t5
forecasting
time series
intermitent demand
text-generation-inference
Instructions to use nieche/chronos-bolt-base-fine-tuned-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nieche/chronos-bolt-base-fine-tuned-v3 with Transformers:
# Load model directly from transformers import AutoTokenizer, ChronosBoltModelForForecasting tokenizer = AutoTokenizer.from_pretrained("nieche/chronos-bolt-base-fine-tuned-v3") model = ChronosBoltModelForForecasting.from_pretrained("nieche/chronos-bolt-base-fine-tuned-v3") - Notebooks
- Google Colab
- Kaggle
Model Card for Chronos Bolt BASE Fine-Tuned Model v3
The model was fine-tuned on a proprietary dataset. Details about the dataset are confidential.
WQL = 0.5082
Summary
The fine-tuned model performs well for intermittent demand forecasts (e.g., Intermittent and Lumpy Time Series demand forecasts for seasonal products).
Technical Specifications
Model Architecture and Objective
The model is based on the amazon/chronos-bolt-base architecture, fine-tuned specifically for intermittent time-series forecasting tasks. It leverages pre-trained capabilities for sequence-to-sequence modeling, adapted to handle multi-horizon forecasting scenarios.
Contact:
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Model tree for nieche/chronos-bolt-base-fine-tuned-v3
Base model
amazon/chronos-bolt-base