influenza_transformer – Boost Time‑Series Forecast Accuracy with PyTorch Transformers
From the repo · “PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"”
The influenza_transformer tool uses a PyTorch implementation of the Transformer model to provide accurate time series forecasting, ideal for sequence data such as influenza prevalence.
- •You have a dataset with sequential time-series data and need to forecast future values
- •Your startup deals with health-related data, such as disease prevalence, and you want to predict outbreaks
- •You're working with data that has strong temporal dependencies, and traditional forecasting methods are not sufficient
python example.py --data_path ./data/ --model_path ./models/ --forecast_steps 30
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/influenza-transformer && curl -fsSL https://workflowstacks.com/api/skills/influenza-transformer/claude-skill -o ~/.claude/skills/influenza-transformer/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Influenza_transformer – Boost Time‑Series Forecast Accuracy with PyTorch Transformers is a small Python project (~950 lines across 7 code files). Setup is light: small project — see the README for how to run it. Last commit 49 months ago, no license file, no tests found.
- 1README.mdStart here — what it does and how to install it
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/influenza-transformer && curl -fsSL https://workflowstacks.com/api/skills/influenza-transformer/claude-skill -o ~/.claude/skills/influenza-transformer/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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