📈 Time Series Analysis: Stock Price Trends

This project presents a detailed time series analysis of selected stock price data using Python. The notebook explores temporal patterns, stationarity, decomposition, and smoothing techniques to uncover insights that support forecasting and investment decisions.

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1. 🧭 Introduction

• Problem Statement: Understand and model the behavior of stock prices over time to identify trends, seasonality, and volatility.
• Objectives: Apply time series techniques to visualize, decompose, and smooth stock price data for actionable insights.
• Data Source: Historical stock price data retrieved from Yahoo Finance using yfinance.
• Context: Time series analysis is vital in financial modeling, risk management, and algorithmic trading. This project demonstrates intermediate-level techniques for exploring stock dynamics.

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2. 🧹 Data Preparation

• Loading: Stock data is loaded and organized into a pivot table.
• Cleaning: Missing values are handled, and date formats are standardized.
• Validation: Data ranges and completeness are verified across all selected stocks.

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3. 📊 Exploratory Analysis

• Visualizations: Individual and comparative time series plots are generated for each stock.
• Statistics: Summary metrics such as mean, variance, and price range are computed.
• Patterns: Initial trends and cyclical behaviors are identified visually.

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4. 📉 Stationarity Analysis

• ADF Test: Augmented Dickey-Fuller test is applied to assess stationarity.
• Visual Checks: Rolling mean and standard deviation plots are used to support statistical findings.
• Transformations: Log transformations or differencing are suggested if non-stationarity is detected.

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5. 🔍 Decomposition Analysis

• Method: seasonal_decompose from statsmodels is used to split the series into:
  • Trend
  • Seasonality
  • Residuals
• Model Rationale: Additive decomposition is chosen based on data characteristics.
• Interpretation: Each component is analyzed to understand its contribution to overall price movement.

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6. 🧠 Advanced Analysis

• Autocorrelation: ACF and PACF plots are used to study lag dependencies.
• Moving Average: 30-day smoothing is applied to highlight long-term trends.
• Seasonal Detection: Repeating patterns are examined across months and quarters.

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7. 💼 Practical Applications

• Investment Insights: Trend analysis supports timing decisions for entry and exit.
• Forecasting: Decomposition and stationarity checks lay the groundwork for ARIMA or Prophet models.
• Risk Management: Understanding volatility and residual noise helps assess uncertainty.

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8. 🧾 Conclusion

• Key Findings:
  • Clear upward trends in selected stocks
  • Seasonal dips and spikes aligned with market cycles
  • Moderate residual noise indicating partial predictability
• Limitations:
  • No forecasting model implemented yet
  • Sentiment analysis not fully integrated
• Recommendations:
  • Extend with ARIMA or Prophet for forecasting
  • Integrate sentiment scores from news headlines or social media
  • Build an interactive dashboard for stakeholder presentation

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🚀 How to Run

# Clone the repository
git clone https://github.com/Eric-Inkoom-Ayitey/Time-Series-Analysis.git
cd Time-Series-Analysis

# Install dependencies
pip install pandas matplotlib statsmodels yfinance

# Launch the notebook
jupyter notebook Time_Series_Analysis.ipynb