algoDinero_gru_forecaster

Algodinero Gru Forecaster - Financial Time Series Forecasting Model

Model Description

This is a GRU neural network model trained for financial time series forecasting. The model is part of the AlgoDinero FinTech Data Curation project and is designed to predict future stock prices and cryptocurrency values.

Model Architecture

• Model Type: GRU
• Input: Time series of closing prices
• Output: Forecasted prices for future time steps
• Training Data: Multi-asset financial data (stocks and cryptocurrencies)

Usage

import torch
import numpy as np
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(repo_id="usairamsaeed/algoDinero_gru_forecaster", filename="model.pkl")

# Load model
with open(model_path, 'rb') as f:
    model_data = pickle.load(f)

# Use with your forecasting pipeline

Training Details

• Training Period: 365 days of historical data
• Assets: AAPL, TSLA, BTC-USD, ETH-USD, MSFT, GOOGL
• Data Preprocessing: Normalized using z-score normalization
• Training Method: Adam optimizer with MSE loss

Model Performance

This model is part of an ensemble forecasting system that combines multiple approaches:

• Moving Average
• ARIMA
• VAR
• MLP
• GRU
• LSTM
• Transformer

The ensemble approach provides robust predictions by combining the strengths of different forecasting methods.

Limitations

• Model performance depends on market conditions
• Historical performance does not guarantee future results
• Use for educational/research purposes
• Not intended for actual trading decisions

Citation

@misc{algoDineroGru,
  title={AlgoDinero GRU Financial Forecasting Model},
  author={Usair Ahmad Saeed},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/usairamsaeed/algoDinero_gru_forecaster}}
}