Create README.md

README.md

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+---
+tags:
+- time-series-prediction
+- transformer
+- cryptocurrency
+- finance
+datasets:
+- crypto-historical-data
+license: mit
+---
+
+# CryptoTS-Transformer-Forecast-7D
+
+## 📝 Overview
+
+**CryptoTS-Transformer-Forecast-7D** is a purpose-built Transformer model designed for financial time series forecasting, specifically predicting the **next 7 days of the closing price** for major cryptocurrencies (e.g., BTC, ETH).
+
+Unlike traditional RNNs, this architecture leverages the attention mechanism to capture long-range dependencies and complex correlations across multiple technical indicators, leading to more robust sequence-to-sequence predictions.
+
+## 🧠 Model Architecture
+
+The model is a custom encoder-decoder Transformer architecture:
+
+* **Type:** Sequence-to-Sequence Time Series Transformer.
+* **Input Sequence Length (`seq_len`):** 60 time steps (representing 60 days of historical data).
+* **Output Sequence Length (`output_len`):** 7 time steps (the 7 days being predicted).
+* **Input Features (`input_size`):** 12 features, including OHLCV data and 7 key technical indicators (MACD, RSI, various moving averages and Bollinger Bands).
+* **Core Components:**
+    * **Positional Encoding:** Added to input embeddings to preserve the temporal order.
+    * **Multi-Head Attention:** Utilized within both the encoder and decoder to weigh the importance of different time steps and features.
+* **Loss Function:** Mean Squared Error (MSE).
+
+## 🚀 Intended Use
+
+* **Financial Forecasting:** Generating forward-looking predictions for high-volatility financial assets.
+* **Algorithmic Trading Signal:** Use the 7-day forecast as input for automated trading strategies.
+* **Risk Assessment:** Modeling potential near-term price volatility based on predicted ranges.
+
+## ⚠️ Limitations
+
+* **Non-Stationarity:** Financial time series are inherently non-stationary. While the model uses robust scaling, sudden market shifts (black swan events, major news) can lead to significant forecast errors.
+* **Data Quality:** The model's performance is heavily reliant on the accuracy and quality of the input features, particularly the technical indicators.
+* **Overfitting:** Due to the complexity of the Transformer, there is a risk of overfitting to historical noise patterns.
+
+## 💻 Example Code
+
+This example assumes a pre-processed input tensor `input_data` (60 days of 12 features) scaled using a standard scaler.
+
+```python
+import torch
+import torch.nn as nn
+from transformers import AutoModel
+
+# NOTE: This model uses a custom class not available in standard transformers,
+# but can be loaded via a custom class or registered in AutoModel.
+# For demonstration, we assume it's loaded as a generic AutoModel.
+model_name = "Your-HF-Username/CryptoTS-Transformer-Forecast-7D"
+model = AutoModel.from_pretrained(model_name)
+model.eval()
+
+# Dummy input data: (Batch_Size, Sequence_Length, Input_Features) -> (1, 60, 12)
+# In a real scenario, this would be 60 days of OHLCV + 7 Indicators, scaled.
+input_data = torch.randn(1, model.config.seq_len, model.config.input_size) 
+
+# Make prediction
+with torch.no_grad():
+    # Output shape will be (Batch_Size, Output_Length, 1) or (1, 7, 1) for the predicted Close price
+    prediction = model(input_data) 
+
+# Convert predicted prices back to their original scale using the inverse transform
+# predicted_prices_7d = scaler.inverse_transform(prediction.cpu().numpy())
+
+print("Input shape (60 days, 12 features):", input_data.shape)
+print("Predicted output shape (7 days, 1 feature/price):", prediction.shape)
+print("Predicted 7-day normalized closing prices:\n", prediction.squeeze().numpy())