Advanced Time Series Forecasting with Deep Learning and Attention Mechanisms
This project implements an advanced multivariate time series forecasting system using deep learning.
Two models are developed and compared: Baseline LSTM Model – a standard sequence-to-one forecasting model Attention-Augmented LSTM Model – enhances interpretability and accuracy by focusing on important historical time steps
The objective is to demonstrate how attention mechanisms improve forecasting performance and provide insight into which past observations influence predictions. Dataset: Electricity Consumption Dataset Source: statsmodels.datasets.electricity Type: Multivariate time series Target: Predict future electricity consumption based on historical observations
Data Preprocessing Time index converted to datetime format Feature scaling using MinMaxScaler Time-series windowing applied using a sliding window approach Input: past 24 time steps Output: next time step prediction
Models Implemented
Baseline Model: LSTM (64 units) Dense output layer Loss function: Mean Squared Error (MSE) Optimizer: Adam
Attention-Augmented Model: LSTM with return_sequences=True Custom attention layer Dense output layer Enables dynamic weighting of historical time steps
Evaluation Methodology Time-aware train/test split (80% train, 20% test)
Evaluation Metrics: RMSE (Root Mean Squared Error) MAE (Mean Absolute Error)
Results The attention-based LSTM model achieved lower RMSE and MAE compared to the baseline LSTM model.
Key Insight: The attention mechanism assigns higher importance to recent and peak-load time steps, allowing the model to focus on the most relevant historical information during forecasting.
Attention Interpretability The attention layer provides interpretability by: Highlighting which time steps influence predictions Reducing noise from less relevant historical data Improving both accuracy and transparency
How to Run the Project
- Install Dependencies pip install statsmodels tensorflow scikit-learn matplotlib
- Run the Notebook Open and execute: Time_Series_Attention_Forecasting.ipynb
Repository Structure advanced-time-series-attention/ │ ├── Time_Series_Attention_Forecasting.ipynb ├── README.md
Technologies Used Python TensorFlow / Keras NumPy Pandas Scikit-learn Statsmodels Matplotlib
Conclusion This project demonstrates that incorporating attention mechanisms into LSTM-based time series models improves forecasting accuracy and interpretability. Attention allows the model to selectively focus on important historical periods, making it more effective than traditional sequence models.
Author Preetha Devi