Stock Market Price Forecasting – IT and Computer Engineering Guide
1. Project Overview
Objective: Develop a system to forecast stock market prices
using time series models such as Long Short-Term Memory (LSTM).
Scope: Predict future stock prices based on historical data, aiding in
financial decision-making and market analysis.
2. Prerequisites
Knowledge: Understanding of Python programming, time series
analysis, and deep learning models, especially LSTM.
Tools: Python, TensorFlow/Keras, NumPy, Pandas, and Matplotlib.
Data: Historical stock price data from sources like Yahoo Finance or Alpha
Vantage.
3. Project Workflow
- Data Collection: Gather historical stock price data.
- Data Preprocessing: Handle missing values, normalize data, and create sequences for time series modeling.
- Model Development: Build and train an LSTM-based model for forecasting.
- Evaluation: Measure the model's performance using metrics like Mean Absolute Error (MAE).
- Visualization: Plot actual vs predicted prices to evaluate the model's accuracy.
4. Technical Implementation
Step 1: Import Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
Step 2: Load and Preprocess Data
# Load dataset
data = pd.read_csv('stock_data.csv')
close_prices = data['Close'].values.reshape(-1, 1)
# Normalize data
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(close_prices)
# Create sequences
sequence_length = 60
X, y = [], []
for i in range(sequence_length, len(scaled_data)):
X.append(scaled_data[i-sequence_length:i, 0])
y.append(scaled_data[i, 0])
X, y = np.array(X), np.array(y)
X = X.reshape(X.shape[0], X.shape[1], 1)
Step 3: Build the LSTM Model
# Initialize model
model = Sequential([
LSTM(50, return_sequences=True,
input_shape=(X.shape[1], 1)),
Dropout(0.2),
LSTM(50, return_sequences=False),
Dropout(0.2),
Dense(25),
Dense(1)
])
# Compile model
model.compile(optimizer='adam', loss='mean_squared_error')
# Train model
model.fit(X, y, batch_size=32, epochs=10, validation_split=0.2)
Step 4: Evaluate the Model
# Prepare test data and predict
predicted_prices = model.predict(X_test)
predicted_prices = scaler.inverse_transform(predicted_prices)
# Plot results
plt.figure(figsize=(14, 5))
plt.plot(data['Close'], label='Actual Prices')
plt.plot(predicted_prices, label='Predicted Prices', linestyle='dashed')
plt.legend()
plt.show()
5. Results and Insights
Analyze the model's predictions against actual stock prices to determine its accuracy. Use evaluation metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
6. Challenges and Mitigation
Volatility: Incorporate additional features like volume or
market sentiment.
Overfitting: Use dropout layers and regularization techniques.
7. Future Enhancements
Incorporate sentiment analysis from financial news to
improve predictions.
Expand to multi-stock prediction for portfolio management.
8. Conclusion
The Stock Market Price Forecasting project leverages time series models to provide valuable insights for investors and financial analysts.