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Check out the documentation for more information.
Stock Price Forecasting Model 📈 Model Description
This model predicts future stock prices based on historical time-series data using LSTM (Long Short-Term Memory) networks. It is trained on publicly available stock price datasets and aims to assist in forecasting stock market trends.
Framework: TensorFlow / PyTorch
Model Type: LSTM-based Recurrent Neural Network
Task: Time-Series Forecasting
Input: Historical stock prices (Open, High, Low, Close, Volume)
Output: Next-day (or multi-step) stock price predictions
Intended Use
For educational and research purposes in financial forecasting.
Not intended for actual financial investment advice 🚫💸.
How to Use Install dependencies pip install torch numpy pandas matplotlib
Example Usage import torch import numpy as np import pandas as pd from model import StockPricePredictor # <- adjust to your class name
Load the model
model = StockPricePredictor() model.load_state_dict(torch.load("stock_price_model.pt", map_location="cpu")) model.eval()
Example input: last 60 days of stock prices (normalized)
example_input = torch.randn(1, 60, 5) # batch_size=1, sequence_length=60, features=5 with torch.no_grad(): prediction = model(example_input)
print("Predicted Next-Day Price:", prediction.item())
Training Details
Training Data: Historical stock datasets (e.g., Yahoo Finance, Kaggle datasets).
Epochs: 50
Optimizer: Adam
Loss Function: MSE (Mean Squared Error)
Evaluation
Metrics used: RMSE, MAE
Performance varies by dataset and stock ticker.