tesla-stock-forecasting

📈 Advanced Time Series Forecasting of Tesla Stock

Comparative Analysis of Classical, Seasonal, and Deep Learning Models with Exogenous Predictors

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🧠 Overview

This project presents a comprehensive time series forecasting framework for Tesla Inc. (TSLA) stock using a range of modeling techniques:

• 📊 Classical statistical models: AR, MA, ARIMA
• 📅 Seasonal models: SARIMA, Seasonal Decomposition
• 🤖 Deep learning: LSTM Neural Networks
• 📈 Exogenous variable-enhanced models: ARIMAX, SARIMAX
• 🧬 Hybrid methodologies: Statistical + Machine Learning combinations

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📌 Project Details

• Data Source: Yahoo Finance (yfinance)
• Ticker: TSLA
• Time Frame: January 2015 – January 2024
• Language: Python
• Type: Time Series Forecasting & Predictive Modeling

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🛠️ Tools & Libraries

• pandas, numpy – Data wrangling and preprocessing
• matplotlib, seaborn, plotly – Visualization
• statsmodels, pmdarima – Classical & Seasonal time series models
• tensorflow, keras – Deep learning (LSTM)
• scikit-learn – Evaluation and scaling
• yfinance – Stock data extraction

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🎯 Objectives

• Perform EDA on Tesla’s historical stock prices & returns
• Build and compare AR, MA, ARIMA, and SARIMA models
• Implement exogenous variable models (e.g., ARIMAX, SARIMAX)
• Train and evaluate deep learning models such as LSTM for sequence prediction
• Compare classical vs deep learning approaches
• Evaluate model performance using:
  • RMSE, MAE, MAPE
  • AIC / BIC
  • Ljung-Box test for residual independence
  • Residual plots and prediction accuracy

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🔍 Model Suite

• AR, MA, ARIMA: Classical univariate models for time series
• SARIMA: Seasonal modeling with trend + seasonality
• ARIMAX / SARIMAX: Includes external regressors
• Seasonal Decomposition: Trend, Seasonality, Residual breakdown
• LSTM (RNN): Deep learning for sequential data
• Hybrid Models: Combinations (e.g., ARIMA + LSTM)

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✅ Results Summary

• Best Classical Model: SARIMA(1,1,1)(1,1,0)[12]
• Best Deep Learning Model: LSTM with TimeDistributed layers
• Best Hybrid: ARIMA + LSTM performed competitively

Key Insights:

• Seasonality and external market signals improved accuracy
• Deep learning models showed strong performance on volatile periods
• Hybrid strategies delivered the most consistent forecasts in noisy datasets

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📁 Project Structure

tesla-stock-forecasting/
├── data/
│   └── raw/
├── src/
│   ├── models/
│   │   ├── classical/
│   │   └── deep_learning/
│   ├── preprocessing/
│   └── visualization/
├── notebooks/
└── results/

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🔮 Future Work

• Integrate Meta Prophet for trend + seasonality forecasting
• Include macroeconomic indicators as exogenous variables
• Deploy a web dashboard for interactive forecast visualization

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📬 Contact

Sospeter Njenga Wainaina
📧 sospeternjenga03@gmail.com
📍 Nairobi, Kenya

🔟 Conclusion & Recommendations

✅ Best Performing Model

After evaluating six models — AR, MA, ARIMA, SARIMA, SARIMAX, and LSTM, and implementing a Hybrid ARIMA + LSTM, we conclude:

🔥 Hybrid ARIMA + LSTM is the most accurate model for forecasting Tesla stock prices.

• It outperformed others in RMSE, MAE, MAPE.
• Statistically significant accuracy advantage confirmed via Diebold-Mariano test vs. SARIMAX (p ≈ 0.0000).

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🧠 Model Strengths by Category

Model	Strengths	Best Use Cases
AR / MA	Simplicity, fast computation	Short-term trend capturing, quick benchmarks
ARIMA	Handles non-stationarity	Medium-term forecasting without seasonality
SARIMA	Accounts for both trend + seasonality	Monthly/quarterly financial patterns
SARIMAX	Integrates external variables like volume or lagged returns	Multivariate forecasting, macro-driven influence
LSTM	Learns complex, nonlinear temporal dependencies	High-volatility prediction, adaptive learning
Hybrid	ARIMA models trend, LSTM models residuals (nonlinear patterns)	Highly volatile, non-linear series like stocks

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⚠️ Limitations

• No macroeconomic data like interest rates or inflation included.
• LSTM training can be time-consuming and sensitive to hyperparameters.
• Assumes stable market conditions, which may not hold in extreme economic events.

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🚀 Future Improvements

• Add exogenous features (e.g., S&P500 index, news sentiment, global indicators).
• Tune deep learning models with Bayesian optimization or Grid Search.
• Explore attention-based architectures like Transformer models for enhanced context learning.
• Deploy as a web app using Streamlit for interactive forecasting.

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