Project: Tesla Stock Price Forecasting Project

Project Overview

This project implements time-series forecasting for Tesla stock prices using two different approaches:

1. ARIMA - Traditional statistical model
2. LSTM - Deep learning neural network

Dataset

• Stock: Tesla (TSLA)
• Period: 2010-2020
• Data Source: Kaggle (https://www.kaggle.com/datasets/timoboz/tesla-stock-data-from-2010-to-2020 )
• Features: Open, High, Low, Close, Adj Close, Volume (focusing on Close prices)

Start Guide

Installation

# Install required packages
pip install -r requirements.txt

Running the Jupyter Notebook

# Open and run the main notebook
jupyter notebook Time_Series_Forecasting.ipynb

📁 Project Structure

├── requirements.txt                # Python dependencies
├── Notebook/
    ├──Time_Series_Forecasting.ipynb   # Main analysis notebook
├── README.md                       # Project documentation
└── outputs/
    ├── tesla_eda.png              # Exploratory data analysis plots
    ├── lstm_training_history.png  # LSTM training progress
    ├── prophet_components.png     # Prophet decomposition
    ├── model_comparison.png       # Performance comparison
    ├── all_models_predictions.png # All predictions visualized
    └── forecasting_report.txt     # Detailed analysis report

Model Descriptions

1. ARIMA (AutoRegressive Integrated Moving Average)

A traditional statistical method for time series forecasting.

How it works:

• AR (AutoRegressive): Uses past values to predict future values
• I (Integrated): Makes data stationary by differencing
• MA (Moving Average): Uses past forecast errors

Parameters (p, d, q):

• p: Number of lag observations
• d: Degree of differencing (to make data stationary)
• q: Size of moving average window

Example: ARIMA(5,1,0) means:

• Use 5 past values
• Difference the data once
• No moving average component

Pros:

• Fast training and prediction
• Interpretable results
• Works well with linear trends

Cons:

• Assumes linear relationships
• Requires stationary data
• May struggle with sudden changes

2. LSTM (Long Short-Term Memory)

A type of recurrent neural network (deep learning).

How it works:

• Has "memory cells" that can remember long-term patterns
• Uses gates to decide what information to keep or forget
• Processes sequences of data (60 days → 1 day prediction)

Architecture:

Input (60 days) → LSTM Layer (50 units) → Dropout (20%) 
→ LSTM Layer (50 units) → Dropout (20%) 
→ Dense Layer (25 units) → Output (1 day)

Key Concepts:

• Sequence Length: Uses 60 days of history to predict next day
• Normalization: Scales prices to 0-1 range for better training
• Dropout: Prevents overfitting by randomly dropping connections

Pros:

• Captures non-linear patterns
• Handles long-term dependencies
• Powerful for complex data

Cons:

• Requires more data
• Longer training time
• Needs GPU for faster training
• Can overfit easily

Performance Metrics Explained

RMSE (Root Mean Squared Error)

• Measures average prediction error in dollars
• Lower is better
• Penalizes large errors more heavily
• Example: RMSE of $5.50 means average error is $5.50

MAE (Mean Absolute Error)

• Average absolute difference between predicted and actual
• Lower is better
• More interpretable than RMSE
• Example: MAE of $4.20 means on average, predictions are $4.20 off

MAPE (Mean Absolute Percentage Error)

• Shows error as a percentage
• Lower is better
• Good for comparing across different price ranges
• Example: MAPE of 3.5% means predictions are off by 3.5% on average

Results Summary

Based on the analysis, here's what each model achieved:

Model	RMSE	MAE	MAPE	Best For
ARIMA	$12.43	$7.68	2.47%	Quick forecasts, linear trends
LSTM	$21.14	$14.26	4.51%	Complex patterns, large datasets

Forecasting results:

Key Code Sections Explained

Rolling Window Evaluation

# This tests the model on new data iteratively
for t in range(len(test_data)):
    model = ARIMA(history, order=(5,1,0))
    model_fit = model.fit()
    prediction = model_fit.forecast()
    # Add actual value to history for next prediction
    history.append(actual_value)

Why? This simulates real-world forecasting where you get new data daily.

Data Normalization for LSTM

scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

Why? Neural networks work better with normalized data (0-1 range).

Sequence Creation for LSTM

# Use past 60 days to predict day 61
X = [day1-60], [day2-61], [day3-62], ...
y = [day61], [day62], [day63], ...

Why? LSTM needs sequences to learn temporal patterns.

Learning Resources

Understanding ARIMA:

• Stationarity: Data should have constant mean and variance
• ACF/PACF Plots: Help determine p and q parameters
• Differencing: Makes non-stationary data stationary

Understanding LSTM:

• Sequences: LSTM processes sequences of data
• Gates: Control information flow (forget, input, output gates)
• Training: Requires many epochs and backpropagation

Important Notes

1. Stock Market Disclaimer:

   • These models are for educational purposes only
   • Stock prices are influenced by many unpredictable factors
   • Never use predictions as sole investment advice

2. Data Quality:

   • More data = better models
   • Tesla's stock has high volatility
   • Consider using multiple years of data

3. Model Selection:

   • No single model is "best" for all situations
   • Consider ensemble methods (combining models)
   • Validate on recent data

4. Hyperparameter Tuning:

   • ARIMA: Try different (p,d,q) combinations
   • LSTM: Adjust layers, units, sequence length
   • Prophet: Modify seasonality and changepoint settings

Next Steps & Improvements

1. Add More Features:

   • Include volume, technical indicators
   • Add sentiment analysis from news
   • Include market indices (S&P 500)

2. Try Ensemble Methods:

   • Combine predictions from all models
   • Weighted average based on performance

3. Real-Time Updates:

   • Fetch latest data automatically
   • Retrain models periodically

4. More Stocks:

   • Extend to multiple stocks
   • Portfolio optimization

Contributing

Feel free to fork this project and submit pull requests!