ai-agent

influenza_transformer: Forecast with Transformers

Name: influenza_transformer: Forecast with Transformers
Author: KasperGroesLudvigsen

Get accurate time series forecasting with this PyTorch Transformer model, ideal for founders working with sequence data

260 stars79 forksPythonGuide quality 9/10Updated 10/18/2022100% free · open source

What it does

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.

When to use it

•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

Ready-to-paste prompt

python example.py --data_path ./data/ --model_path ./models/ --forecast_steps 30

Heads up: You need to have a compatible PyTorch version (1.9.0) installed, as specified in the README, to avoid compatibility issues

Saves to your device

Use with Claude

New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install

Claude Code

mkdir -p ~/.claude/skills/influenza-transformer && curl -fsSL https://workflowstacks.com/api/skills/influenza-transformer/claude-skill -o ~/.claude/skills/influenza-transformer/SKILL.md

Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)

claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How influenza_transformer: Forecast with Transformers works

Codeflow

Free to inspect

Influenza_transformer: Forecast with 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 47 months ago, no license file, no tests found.

Size

Small codebase

~950 lines · 7 code files · 8 min skim

Setup

Light setup

Small project — see the README for how to run it.

Runs on

Python

No API keys detected

Python 100%

Where to start reading

1
README.md
Start here — what it does and how to install it

What's in each folder

data/Data files1 files

READMENo tests foundThin docsNo CINo licenseLast update 47 mo ago