Add model card README

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+- time-series
+- forecasting
+- lstm
+- multi-task
+- multi-domain
+pipeline_tag: time-series-forecasting
+---
+
+# Future Prediction Models (Multi-Domain LSTM)
+
+Trained PyTorch LSTM checkpoints forecasting 7 daily time-series domains (AI/NVIDIA, Programming/npm, Finance/BTC, Sports/ATP Elo, Weather/Chennai, Economy/S&P500, Energy/WTI).
+
+## Checkpoints
+
+All models predict 7 steps from a 60-step window of base-normalized values `x_t = value_t / value_{t-1} - 1`.
+
+| File | Parameters (hidden, layers, dropout) |
+|---|---|
+| `unified_model.pt` | Multi-task: shared LSTM (128, 2) + domain embedding (16) + 7 heads; domains: ai, programming, finance, sports, weather, economy, energy |
+| `model_ai.pt` | 128 hidden, 3 layers, dropout 0.1 |
+| `model_finance.pt` | 128 hidden, 3 layers, dropout 0.1 |
+| `model_programming.pt` | 128 hidden, 3 layers, dropout 0.1 |
+| `model_sports.pt` | 128 hidden, 3 layers, dropout 0.1 |
+| `model_economy.pt` | 64 hidden, 2 layers, dropout 0.1 |
+| `model_energy.pt` | 64 hidden, 2 layers, dropout 0.1 |
+| `model_weather.pt` | 64 hidden, 2 layers, dropout 0.1 |
+
+## Validated accuracy (held-out, H1 MAPE)
+
+| Topic | Separate | Unified |
+|---|---|---|
+| AI | 1.86% | 2.36% |
+| Programming | 7.12% | 5.42% |
+| Finance | 1.52% | 1.74% |
+| Sports | 0.06% | 0.04% |
+| Weather | 0.18% | 0.18% |
+| Economy | 0.64% | 0.75% |
+| Energy | 2.83% | 1.90% |
+
+## Model architecture (reference)
+
+```python
+class LSTMForecaster(nn.Module):
+    def __init__(self, input_size=1, hidden_size=128, num_layers=3, dropout=0.1, horizon=7):
+        ...
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=...)
+        self.head = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.ReLU(),
+                                  nn.Dropout(dropout), nn.Linear(hidden_size, horizon))
+    def forward(self, x):  # x: (B, 60, 1)
+        out, _ = self.lstm(x)
+        return self.head(out[:, -1, :])  # (B, 7)
+```
+
+Forecasts are statistical estimates on validated hold-out data; no model can predict the future with 100% accuracy.
+
+## License
+
+MIT