Stock Price Prediction using Time Series Analysis

A Predictive Model for Stock Price Forecasting Using Auto-ARIMA to Assist Investors in Making Data-Driven Decisions.

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Table of Contents

• Project Overview
• Features
• Tech Stack
• Data Pipeline
• Modeling
• Evaluation Metrics
• Setup Instructions
• Usage
• Future Enhancements
• Contributing
• License

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Project Overview

The Stock Price Prediction project leverages time series forecasting models to predict the future prices of stocks, enabling investors and traders to make informed, data-driven decisions. By analyzing historical stock prices, the model identifies patterns and trends, helping to anticipate future market movements.

The key goals of this project are:

• To provide reliable stock price predictions using time series models.
• To ensure data integrity and high-quality analysis for accurate forecasting.
• To enable investors to optimize their investment strategies based on predictions.

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Features

• Time Series Forecasting: Built using Auto-ARIMA, the project can predict future stock prices based on historical data.
• Data Visualization: The app provides visualizations of stock price trends, patterns, and predictions.
• Investor Insights: Helps investors make data-driven decisions by analyzing stock trends over time.
• Model Evaluation: Assesses the accuracy of stock predictions using standard evaluation metrics like MAPE and RMSE.

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Tech Stack

• Backend: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Statsmodels (for Auto-ARIMA), Scikit-learn
• Time Series Analysis: Auto-ARIMA, ADF Test (for stationarity)
• Visualization: Matplotlib, Seaborn
• Deployment: Jupyter Notebooks (for development), Flask (for web deployment, optional)

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Data Pipeline

1. Data Collection: Historical stock price data is gathered from publicly available sources (e.g., Yahoo Finance, Alpha Vantage API).

2. Data Preparation:

   • Missing Value Handling: Ensures no missing or anomalous values in the stock price data.
   • Feature Engineering: Generated useful features such as stock price returns, moving averages, and volatility for better model accuracy.

3. Exploratory Data Analysis (EDA):

   • Visualized stock price trends over time.
   • Performed stationarity tests (ADF Test) to ensure that the data is suitable for time series modeling.
   • Identified seasonality and trends in the data using moving averages.

4. Data Transformation:

   • Differencing: Applied differencing to make the time series stationary.
   • Log Transformation: Used to stabilize the variance in stock prices.
   • Scaling: Normalized the data for better performance of the ARIMA model.

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Modeling

1. Auto-ARIMA Model

• Automatically identifies the best parameters (p, d, q) for the ARIMA model based on the stock price data.
• Trains a forecasting model on historical data and predicts future stock prices.

2. Model Training:

• The Auto-ARIMA algorithm automatically tunes the model's parameters to fit the time series data optimally.
• Trained on historical stock prices to predict future prices for a specified time horizon (e.g., 30 days ahead).

3. Model Validation:

• The model's performance is validated using cross-validation on different slices of the time series data.
• Forecasted results are compared to actual stock prices to assess accuracy.

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Evaluation Metrics

The model is evaluated using the following metrics to ensure high-quality and accurate stock price predictions:

• Mean Absolute Percentage Error (MAPE): Measures the accuracy of predictions relative to actual values.
• Root Mean Squared Error (RMSE): Measures the deviation between predicted stock prices and actual prices.
• R² Score: Assesses how well the model captures the variability of stock prices.

The Auto-ARIMA model provided a robust R² score and performed exceptionally well, giving reliable predictions for future stock prices.

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Setup Instructions

Prerequisites

• Python 3.7+
• Required libraries: Pandas, NumPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn

Installation

1. Clone the repository: git clone https://github.com/SamJoeSilvano/Stock_Price_Time_Series_Analysis.git

2. Navigate to the project directory: cd stock-price-prediction

3. Install the dependencies: pip install -r requirements.txt

4. Run the Jupyter Notebook or Flask app (optional): jupyter notebook

   or

   python app.py

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Usage

1. Load Data: Import historical stock price data in CSV format.
2. Visualize Trends: Generate visualizations for historical data and moving averages to spot trends.
3. Train Model: The Auto-ARIMA model automatically trains and predicts future stock prices based on the time series data.
4. Predict: Use the trained model to forecast stock prices for the next n days.
5. Evaluation: Compare the model's predictions with actual stock prices to gauge its accuracy.

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Future Enhancements

• Incorporate More Models: Introduce LSTM or Prophet models for more accurate and diverse predictions.
• Interactive Dashboard: Create an interactive web-based dashboard where users can visualize stock price trends and predictions in real-time.
• Advanced Feature Engineering: Introduce additional features such as macroeconomic indicators, sentiment analysis, or technical indicators for better predictions.
• Real-Time Stock Price Updates: Fetch real-time stock data using an API to allow continuous stock price predictions.

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Contributing

Contributions are always welcome! Here's how you can help:

1. Fork the project.
2. Create a new feature branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Acknowledgements

• Special thanks to the Pandas and Statsmodels communities for their excellent resources.
• This project was inspired by the growing interest in algorithmic trading and stock price forecasting.