Time Cross Adaptive Self Attention( TCSA ) based Imputation model

Time-Cross Adaptive Self-Attention (TCSA) model for multivariate Time Series Imputation Model for Sparse and Irregular Data

model Architecture

⏳ Time-Cross Adaptive Self-Attention (TCSA) for Multivariate Time Series Imputation

This repository presents a novel deep learning architecture—Time-Cross Adaptive Self-Attention (TCSA)—designed for imputing missing values in sparse and irregular multivariate time series data. The model leverages temporal and cross-variable dependencies using adaptive attention mechanisms, making it ideal for real-world datasets with non-uniform sampling and high missingness.

📌 Project Overview

Multivariate time series data often suffer from missing values due to sensor failures, irregular sampling, or transmission errors. Traditional imputation methods fail to capture temporal dynamics and inter-variable relationships. TCSA addresses this by:

• Modeling both temporal and cross-variable dependencies
• Using adaptive self-attention to weigh relevant time steps and features
• Handling sparse and irregular data without interpolation

🧠 Model Architecture

The TCSA model consists of the following components:

🔹 Temporal Encoder

• Captures intra-variable temporal patterns
• Uses self-attention over time steps
• Learns dynamic temporal weights

🔹 Cross-Variable Encoder

• Captures inter-variable correlations
• Applies attention across feature dimensions
• Enables adaptive feature fusion

🔹 Fusion Layer

• Combines temporal and cross-variable embeddings
• Applies residual connections and layer normalization

🔹 Imputation Head

• Predicts missing values using fused representations
• Optimized with masked loss functions to focus on missing entries

🚀 Technologies Used

• PyTorch: Core deep learning framework
• NumPy & Pandas: Data manipulation and preprocessing
• Matplotlib: Visualization of imputation performance
• Jupyter Notebook: Interactive experimentation
• Python Scripts: Modular training and evaluation

📁 Repository Structure

├── DataSet_file/                   # Raw and processed time series data
├── Modeling/                       # TCSA model implementation
├── dataset_creation_script/       # Scripts for generating synthetic or real datasets
├── config.py                       # Hyperparameter configuration
├── plot.py                         # Visualization utilities
├── random.ipynb                    # Exploratory analysis and testing
├── test.ipynb                      # Evaluation notebook
├── requirement.txt                 # Python dependencies
├── LICENSE                         # GPL-3.0 license
├── README.md                       # Project documentation

🛠️ How to Install

To get started, clone the repository and set up your environment:

git clone https://github.com/arvind207kumar/Time-Cross-Adaptive-Self-Attention-TCSA-based-Imputation-model-.git
cd Time-Cross-Adaptive-Self-Attention-TCSA-based-Imputation-model-

Install all required Python packages using the provided requirement.txt file:

## 📦 Install Dependencies
pip install -r requirement.txt

📁 Prepare the Dataset

To prepare your dataset:

• Use scripts in the dataset_creation_script/ folder to generate or format time series data.
• Place the processed dataset files into the DataSet_file/ directory.
• You can use synthetic data or real-world datasets depending on your use case.

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🚀 Train and Evaluate the Model

To train and evaluate the TCSA model:

• Open and run random.ipynb for exploratory training and visualization.

• Use test.ipynb for structured evaluation and performance metrics.

• Modify config.py to adjust hyperparameters like:

  • Learning rate
  • Batch size
  • Number of epochs

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📊 Results

The TCSA model demonstrates strong performance across multiple benchmarks:

• ✅ Imputation Accuracy: High fidelity reconstruction of missing values across multiple datasets
• 🔍 Robustness: Handles varying levels of sparsity and irregularity
• 📈 Visualization: Clear plots showing original vs imputed sequences
• 🧪 Generalization: Performs well across synthetic and real-world time series

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🔮 Future Work

Planned enhancements and extensions include:

• 📦 Model Export: Convert to TorchScript or ONNX for deployment
• 🧠 Multivariate Forecasting: Extend TCSA for predictive modeling
• 🧪 Benchmarking: Compare against state-of-the-art imputation baselines
• 🌍 Domain Adaptation: Apply to healthcare, finance, and IoT datasets
• 📱 Interactive Dashboard: Build a Streamlit app for visual imputation