---
tags:
- time-series-forecasting
- stock-prediction
- nse
- card
- transformer
license: apache-2.0
---

# CARD NSE Stock Price Predictor

**Based on:** [CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting](https://arxiv.org/abs/2305.12095) (ICLR 2024)

## Model Description

This model uses the CARD (Channel Aligned Robust Blend) Transformer architecture to predict NSE (National Stock Exchange of India) stock prices. Key innovations from the paper:

- **Dual Attention**: Channel-aligned attention (cross-stock) + Temporal attention (over time) with EMA smoothing
- **Token Blend**: Multi-scale token representations via head-merging
- **Signal Decay Loss**: Weighted MSE that prioritizes near-future accuracy (l^{-1} weighting for stocks)
- **RevIN**: Instance normalization for handling non-stationary financial data
- **Dynamic Projection**: Efficient channel attention via softmax-normalized projection

## Architecture Details

| Parameter | Value |
|-----------|-------|
| Lookback Window | 96 trading days |
| Prediction Horizon | 5 trading days |
| Hidden Dim (d_model) | 64 |
| FFN Dim (d_ff) | 128 |
| Attention Heads | 8 |
| Encoder Layers | 2 |
| Patch Length | 16 |
| Stride | 8 |
| Token Blend Size | 2 |
| EMA Alpha | 0.9 |
| Dynamic Projection Rank | 8 |
| Dropout | 0.3 |

## Test Results

| Metric | Value |
|--------|-------|
| MSE | 0.039362 |
| MAE | 0.132980 |
| RMSE | 0.198398 |

## Stocks Covered

RELIANCE, TCS, HDFCBANK, INFY, ICICIBANK, HINDUNILVR, SBIN, BHARTIARTL, KOTAKBANK, ITC, LT, AXISBANK, BAJFINANCE, ASIANPAINT, MARUTI, HCLTECH, SUNPHARMA, TITAN, ULTRACEMCO, WIPRO

## Training Details

- **Data**: NIFTY 50 stocks via yfinance (Close prices)
- **Split**: 70% train / 15% validation / 15% test
- **Optimizer**: Adam with cosine LR decay + linear warmup
- **Loss**: Signal Decay Loss (l^{-1} weighting)
- **Learning Rate**: 0.0001
- **Epochs**: 100 (with early stopping, patience=10)
- **Batch Size**: 128

## Usage

```python
import torch, json
from card_nse_predictor import CARD, CARDConfig

# Load config
with open("config.json") as f:
    cfg = CARDConfig(**json.load(f))

# Load model
model = CARD(cfg)
model.load_state_dict(torch.load("model.pt", map_location="cpu"))
model.eval()

# Input: (batch, n_stocks, lookback_days) — raw close prices
# Output: (batch, n_stocks, pred_len) — predicted close prices
x = torch.randn(1, 20, 96)
pred = model(x)
print(pred.shape)  # (1, 20, 5)
```

## Citation

```bibtex
@inproceedings{xue2024card,
  title={CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting},
  author={Xue, Wang and Zhou, Tian and Wen, Qingsong and Gao, Jinyang and Ding, Bolin and Jin, Rong},
  booktitle={International Conference on Learning Representations},
  year={2024}
}
```