Stock Market Predictor
Overview
This model is a fine-tuned ProphetNet designed for predicting stock market trends. It takes historical stock data as input and generates forecasts for future price movements, including upward, downward, or stable trends. Trained on extensive financial datasets, it provides probabilistic predictions to aid in investment decisions.
Model Architecture
The model utilizes the ProphetNet architecture, which is an encoder-decoder model optimized for sequence-to-sequence tasks. It features 12 encoder and 12 decoder layers, each with a hidden size of 1024. The model incorporates n-gram attention mechanisms for improved long-range dependency handling in time-series data.
Intended Use
This model is intended for financial analysis applications, such as:
- Predicting short-term stock price trends for traders.
- Portfolio management tools to assess market risks.
- Economic forecasting systems integrating stock data.
It can be used with the Hugging Face Transformers library. Example usage:
from transformers import pipeline
predictor = pipeline("text2text-generation", model="your-username/stock-market-predictor")
result = predictor("Historical data: AAPL prices from 2020-2025.")
print(result)
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