Summary

Added detailed Korean documentation (TimeXer_코드_설명.md) that comprehensively explains how the TimeXer model works, including vector operations, tensor transformations, and the operational principles behind the architecture. This documentation is designed to help users understand and present the codebase.

What's New

A new markdown file TimeXer_코드_설명.md has been created that provides:

📚 Complete Architecture Overview

• Explanation of endogenous and exogenous variables in time series forecasting
• Visual diagrams showing the complete data flow from input to prediction
• Core concepts: patch-based representation, variate-level representation, and global token mechanism

🔢 Detailed Tensor Dimension Tracking

Each processing step is explained with specific tensor dimension examples:

Input:          [32, 168, 3]    # Batch 32, Sequence 168, Variables 3
↓ Patching:     [32, 1, 7, 24]  # 7 patches of length 24
↓ Embedding:    [32, 1, 8, 512] # +1 global token, d_model=512
↓ Encoder:      [32, 8, 512]    # Batch merged
↓ Prediction:   [32, 24, 1]     # Output 24 timesteps

🧩 Component Explanations

1. Patching Mechanism: How long sequences are divided into manageable patches
2. Global Token: The bridge connecting endogenous and exogenous information
3. EnEmbedding: Patch-level representation with positional encoding
4. DataEmbedding_inverted: Variate-level representation for exogenous variables
5. EncoderLayer:
   • Self-Attention for intra-endogenous temporal dependencies
   • Cross-Attention for exogenous-to-endogenous information transfer
6. Prediction Head: Flattening and linear projection to generate forecasts

💡 Practical Examples

Includes a concrete electricity price forecasting scenario:

• 168 hours of historical data → 24 hours prediction
• 1 endogenous variable (price) + 2 exogenous variables (demand, renewable energy)
• Step-by-step walkthrough with actual tensor shapes at each stage

📖 Documentation Structure

1. 개요 (Overview) - Core concepts and terminology
2. 전체 아키텍처 (Overall Architecture) - Visual flowchart
3. 데이터 흐름과 텐서 차원 변화 (Data Flow & Tensor Dimensions) - Detailed step-by-step transformations
4. 주요 컴포넌트 상세 설명 (Component Details) - In-depth technical explanations
5. 실행 순서 (Execution Flow) - Complete forward pass walkthrough
6. 요약 (Summary) - Key design principles and innovations

Use Case

This documentation is ideal for:

• 🎓 Academic presentations and code walkthroughs
• 📊 Understanding the model architecture before implementation
• 🔍 Debugging and tracing tensor transformations
• 📝 Learning how TimeXer integrates exogenous variables into forecasting

Technical Details

The documentation explains:

• How TimeXer employs dual embedding strategies (patch-level for endogenous, variate-level for exogenous)
• The role of the global token in aggregating and bridging information
• Attention mechanism formulas and their specific applications
• Normalization and de-normalization processes
• Efficient attention computation strategy

All explanations include mathematical formulas, code snippets, and dimension tables for clarity.

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