CodeDevX/future-prediction-multi-domain-lstm

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)

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