Time Series Forecasting
Transformers
Safetensors
t5
text2text-generation
TSFM
Finance
Financial Forecasting
FinText
text-generation-inference
Instructions to use FinText/Chronos_Small_2017_Global with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FinText/Chronos_Small_2017_Global with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FinText/Chronos_Small_2017_Global") model = AutoModelForSeq2SeqLM.from_pretrained("FinText/Chronos_Small_2017_Global") - Notebooks
- Google Colab
- Kaggle
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## Chronos-Small (TSFM) — Global (2017)
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This is the **Time Series Foundation Model (TSFM)**, pre-trained on **global financial time series data up to the year 2017** using the **Chronos architecture (Small size)**. The dataset spans from **1990–2017** and includes all **global excess return data**.
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---
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pipeline_tag: time-series-forecasting
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license: apache-2.0
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tags:
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- TSFM
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- Finance
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- Financial Forecasting
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- FinText
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library_name: transformers
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---
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## Chronos-Small (TSFM) — Global (2017)
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This is the **Time Series Foundation Model (TSFM)**, pre-trained on **global financial time series data up to the year 2017** using the **Chronos architecture (Small size)**. The dataset spans from **1990–2017** and includes all **global excess return data**.
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