Stock Price Prediction Using Time Series and Neural Networks

This project is a Jupyter Notebook implementation for predicting stock prices using a time-series approach. The notebook leverages libraries such as pandas, numpy, and matplotlib for data handling and visualization, and TensorFlow for creating and training the neural network model.

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Table of Contents

1. Overview
2. Dataset
3. Installation
4. Usage
5. Key Steps in the Notebook
6. Model Details
7. Results
8. Future Improvements
9. License

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Overview

This notebook demonstrates how to apply time-series analysis and neural networks to forecast future stock prices. The workflow includes:

• Preprocessing the stock price data.
• Visualizing trends in the data.
• Normalizing the dataset.
• Training a neural network for time-series predictions.
• Evaluating model performance on unseen data.

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Dataset

The project uses stock price data stored locally or fetched externally. Ensure your dataset has columns such as:

• Date: The date of the stock price observation.
• Open, High, Low, Close: Stock price metrics.
• Volume: Volume of stock traded.

Example Dataset Format:

Date	Open	High	Low	Close	Volume
2022-01-01	100.5	105.0	99.0	103.5	1500000
2022-01-02	103.5	107.5	102.0	106.0	1700000

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Installation

1. Clone this repository to your local machine.
2. Create a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`

3. Install the required Python packages:

   pip install -r requirements.txt

4. Ensure Jupyter Notebook is installed:

   pip install notebook

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Usage

1. Open the Jupyter Notebook:

   jupyter notebook

2. Navigate to and open Stock_Price_Prediction_Time_Series_NN.ipynb.
3. Follow the step-by-step cells to:
   • Preprocess the dataset.
   • Train the neural network model.
   • Evaluate and visualize predictions.

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Key Steps in the Notebook

1. Import Libraries

Essential libraries like pandas, numpy, matplotlib, and TensorFlow are imported.

2. Load and Preprocess Data

• Load the dataset and check for null values or inconsistencies.
• Normalize the data to improve the model's performance.

3. Create Time-Series Data

• Convert stock prices into a supervised learning format by defining X (inputs) and y (outputs).

4. Build the Neural Network Model

• Use TensorFlow/Keras to create a Sequential model with layers such as:
  • LSTM or GRU for handling time-series patterns.
  • Dense layers for prediction output.

5. Train the Model

• Use the training data to fit the model over several epochs.
• Monitor the training loss and validation loss.

6. Evaluate and Visualize

• Generate predictions for the test set.
• Plot actual vs. predicted stock prices to analyze performance.

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Model Details

The neural network is designed for time-series forecasting and includes:

• Input Layer: Time-series data.
• Hidden Layers: Recurrent layers like LSTM or GRU, followed by Dense layers.
• Output Layer: Single value prediction for the next time step.

Hyperparameters:

• Epochs: Variable based on dataset size (e.g., 50-100).
• Batch size: E.g., 32.
• Optimizer: Adam.

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Results

The project evaluates model performance using metrics like Mean Absolute Error (MAE) and plots to visualize predictions vs. actual data. Ensure sufficient training data for better accuracy.

Example Output:

• Training Loss: 0.05
• Validation Loss: 0.08
• MAE: 1.20

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Future Improvements

1. Dataset Expansion: Use larger datasets with more diverse stock data.
2. Feature Engineering: Incorporate technical indicators like moving averages or RSI.
3. Model Tuning: Experiment with advanced architectures like Transformers for better performance.

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License

This project is open-source and available under the MIT License.