price-forecast-model

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.