Time Series Forecasting in Python

A repository containing an advanced time series forecasting project with LSTM, XGBoost, and Ensemble Learning.

🔹 Overview

This project focuses on predicting time series data using a combination of machine learning and deep learning techniques. It incorporates feature engineering, hyperparameter tuning, and ensemble learning for robust forecasting.

✅ Key Features:

• Time Series Forecasting using XGBoost, LSTM, and GRU 📈
• Feature Engineering (Lag Features, EWMA, Seasonal Features) 🛠️
• Hyperparameter Optimization with Optuna 🔍
• Walk-Forward Validation to prevent data leakage 🏆
• Stacking Ensemble Model for better accuracy 🔗
• Model Interpretability with SHAP & Permutation Importance 📊
• Interactive Visualizations using Plotly 📌

🔹 Tech Stack

• 🟢 Python (Pandas, NumPy, Sci-kit Learn, TensorFlow, Optuna, SHAP, Plotly)
• 🟢 Jupyter Notebook / Google Colab
• 🟢 Libraries: XGBoost, LightGBM, Ridge Regression, MinMaxScaler, StandardScaler

🔹 Feature Engineering

✅ Lag Features (1-day, 7-day, 30-day)
✅ Exponential Weighted Moving Averages (EWMA)
✅ Seasonality Features (Sine & Cosine Transform)
✅ Day of the Week, Quarter, and Weekend Indicators

🔹 Installation & Setup

# Clone the repository
git clone https://github.com/your-username/time_series_forecasting.git

# Navigate to the project folder
cd time_series_forecasting

# Install dependencies
pip install -r requirements.txt

🔹 How to Use?

1. Load the dataset (gold_monthly_csv.csv)
2. Preprocess and scale data
3. Train XGBoost and LSTM models
4. Generate predictions using stacked ensemble learning
5. Evaluate models using RMSE, MAE, R², MAPE
6. Visualize results with Plotly

🔹 Model Performance & Evaluation

📌 Metrics Used: MAE, RMSE, R², MAPE
📌 Validation Strategy: Walk-Forward Cross-Validation
📌 Hyperparameter Tuning: Optuna for XGBoost & LSTM

🔹 Example Code: LSTM Training

lstm_model = Sequential([
    Bidirectional(LSTM(100, return_sequences=True, input_shape=(sequence_length, X_train.shape[1]))),
    Dropout(0.2),
    GRU(100, return_sequences=False),
    Dropout(0.2),
    Dense(1)
])

lstm_model.compile(optimizer='adam', loss='mse')
lstm_model.fit(generator, epochs=20, verbose=1)

🔹 Future Improvements

✅ Optimize LSTM with GRU for better efficiency
✅ Try Attention Mechanism for time series forecasting
✅ Improve hyperparameter tuning using Bayesian Optimization
✅ Deploy model using Flask / FastAPI

🔹 Contributing

Want to contribute? Follow these steps:

1. Fork the repository
2. Create a new branch (feature-xyz)
3. Commit changes
4. Push to the branch
5. Open a Pull Request

🔹 Contact

🔗 [LinkedIn](# Time Series Forecasting in Python

A repository containing an advanced time series forecasting project with LSTM, XGBoost, and Ensemble Learning.

🔹 Overview

This project focuses on predicting time series data using a combination of machine learning and deep learning techniques. It incorporates feature engineering, hyperparameter tuning, and ensemble learning for robust forecasting.

✅ Key Features:

• Time Series Forecasting using XGBoost, LSTM, and GRU 📈
• Feature Engineering (Lag Features, EWMA, Seasonal Features) 🛠️
• Hyperparameter Optimization with Optuna 🔍
• Walk-Forward Validation to prevent data leakage 🏆
• Stacking Ensemble Model for better accuracy 🔗
• Model Interpretability with SHAP & Permutation Importance 📊
• Interactive Visualizations using Plotly 📌

🔹 Tech Stack

• 🟢 Python (Pandas, NumPy, Sci-kit Learn, TensorFlow, Optuna, SHAP, Plotly)
• 🟢 Jupyter Notebook / Google Colab
• 🟢 Libraries: XGBoost, LightGBM, Ridge Regression, MinMaxScaler, StandardScaler

🔹 Feature Engineering

✅ Lag Features (1-day, 7-day, 30-day)
✅ Exponential Weighted Moving Averages (EWMA)
✅ Seasonality Features (Sine & Cosine Transform)
✅ Day of the Week, Quarter, and Weekend Indicators

🔹 Installation & Setup

# Clone the repository
git clone https://github.com/your-username/time_series_forecasting.git

# Navigate to the project folder
cd time_series_forecasting

# Install dependencies
pip install -r requirements.txt

🔹 How to Use?

1. Load the dataset (gold_monthly_csv.csv)
2. Preprocess and scale data
3. Train XGBoost and LSTM models
4. Generate predictions using stacked ensemble learning
5. Evaluate models using RMSE, MAE, R², MAPE
6. Visualize results with Plotly

🔹 Model Performance & Evaluation

📌 Metrics Used: MAE, RMSE, R², MAPE
📌 Validation Strategy: Walk-Forward Cross-Validation
📌 Hyperparameter Tuning: Optuna for XGBoost & LSTM

🔹 Example Code: LSTM Training

lstm_model = Sequential([
    Bidirectional(LSTM(100, return_sequences=True, input_shape=(sequence_length, X_train.shape[1]))),
    Dropout(0.2),
    GRU(100, return_sequences=False),
    Dropout(0.2),
    Dense(1)
])

lstm_model.compile(optimizer='adam', loss='mse')
lstm_model.fit(generator, epochs=20, verbose=1)

🔹 Future Improvements

✅ Optimize LSTM with GRU for better efficiency
✅ Try Attention Mechanism for time series forecasting
✅ Improve hyperparameter tuning using Bayesian Optimization
✅ Deploy model using Flask / FastAPI

🔹 Contributing

Want to contribute? Follow these steps:

1. Fork the repository
2. Create a new branch (feature-xyz)
3. Commit changes
4. Push to the branch
5. Open a Pull Request

🔹 Contact

📩 Email: your-email@example.com
🔗 Linkedin