hmnshudhmn24/future-trend-forecaster

Future Trend Forecaster 📈🔮

FutureTrendForecaster is a time-series forecasting model that predicts emerging technology and research trends before they reach peak hype.

It combines signals from research activity, technology news, and social signals to identify early momentum and forecast future growth.

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🔍 What Problem Does It Solve?

Most trend analysis is reactive — it identifies trends after they become popular.

FutureTrendForecaster focuses on:

• Early-stage signals
• Momentum detection
• Forward-looking forecasting

This makes it useful for strategy, innovation planning, and foresight systems.

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✨ Key Features

• 📊 Multi-source signal ingestion
• 🧠 Time-series feature engineering
• 🔮 Forecasting across future horizons
• 📈 Momentum & trend direction detection
• 🤗 Hugging Face–ready (time-series-forecasting)
• 🎛️ Gradio demo included
• 🧪 Test-covered core logic

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📂 Project Structure

future-trend-forecaster/
├── config/
├── data/
├── src/
├── training/
├── pipelines/
├── scripts/
├── tests/
├── notebooks/
├── app.py
├── README.md
├── model_card.md
├── requirements.txt
└── LICENSE

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⚙️ Installation

pip install -r requirements.txt

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🚀 Quick Usage

from src.inference import FutureTrendPipeline

pipeline = FutureTrendPipeline()

result = pipeline(
    "data/signals/research_trends.csv",
    horizon=6
)

print(result)

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🎛️ Gradio Demo

Run locally:

python app.py

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🧠 How It Works

1. Signal Ingestion – Loads time-series data from multiple sources
2. Feature Engineering – Extracts momentum & trend features
3. Trend Modeling – Generates forward forecasts
4. Forecast Aggregation – Produces explainable results