Temporal Fusion Transformer AI Model for Markets

Development of AI-based Temporal Fusion Transformer Model for Markets

Temporal Fusion Transformer (TFT) — an architecture developed at Google Brain specifically for time series forecasting tasks with heterogeneous input data. Unlike vanilla Transformer, TFT explicitly processes three types of variables: static (unchanging over time), known future (known in advance) and unknown (observed only until the forecast moment).

What Makes TFT Special for Finance

Three categories of input variables:

This is fundamentally important: knowing that the Federal Reserve meeting will occur in 5 days, the model should account for this when forecasting right now. TFT does this explicitly.

Variable Selection Network (VSN): Learnable weights for each input variable. Allows automatic filtering of irrelevant features and provides interpretability — which variables are actually important for the forecast.

Gated Residual Network (GRN): Nonlinear processing with a gate mechanism controlling how much nonlinear transformation is applied (gate = 0: pass-through, gate = 1: full nonlinear).

Complete TFT Architecture

Static covariates → Static Covariate Encoders
                         ↓
Past observed → LSTM encoder ─────────────┐
                                           ├→ Multi-head Attention → GRN → Quantile Output
Known future → LSTM decoder ──────────────┘

Inside attention: temporal self-attention, where each forecast step can "look" at relevant history.

Implementation for Market Data

from pytorch_forecasting import TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.metrics import QuantileLoss

data = prepare_market_dataframe(
    tickers=['AAPL', 'MSFT', ...],  # 100+ instruments
    start='2015-01-01'
)

training = TimeSeriesDataSet(
    data[data.date < '2022-01-01'],
    time_idx="time_idx",
    target="forward_5d_return",
    group_ids=["ticker"],
    max_encoder_length=126,     # 6 months history
    max_prediction_length=5,    # 5 days forecast
    static_categoricals=["sector", "country"],
    static_reals=["log_market_cap", "beta"],
    time_varying_known_reals=["days_to_earnings", "fomc_flag", "vix"],
    time_varying_unknown_reals=[
        "return", "volume_ratio", "rsi", "atr_normalized",
        "momentum_12_1", "short_interest_ratio"
    ],
)

tft = TemporalFusionTransformer.from_dataset(
    training,
    learning_rate=0.001,
    hidden_size=160,
    attention_head_size=4,
    dropout=0.1,
    hidden_continuous_size=64,
    loss=QuantileLoss(quantiles=[0.1, 0.25, 0.5, 0.75, 0.9])
)

Training and Hyperparameters

Key hyperparameters:

• hidden_size: 64-256 (main model capacity)
• attention_head_size: 1-4
• max_encoder_length: 60-252 (1 quarter — 1 year)
• dropout: 0.05-0.3

Learning rate finding:

res = trainer.tuner.lr_find(
    tft, train_dataloaders=train_dataloader,
    val_dataloaders=val_dataloader,
    max_lr=0.1
)
optimal_lr = res.suggestion()

Early stopping:

from pytorch_lightning.callbacks import EarlyStopping
early_stop_callback = EarlyStopping(
    monitor="val_loss", patience=10, mode="min"
)

Quantile Forecasts and Applications

TFT natively outputs quantile forecasts (p10, p25, p50, p75, p90). This is valuable for:

Risk-based position sizing:

point_forecast = forecasts['p50']
uncertainty = forecasts['p90'] - forecasts['p10']
position_size = base_size × (1 / (uncertainty / expected_return))

Asymmetric return profiles: If p90 − p50 >> p50 − p10 → right-skewed distribution → upside potential exceeds risk.

Interpretability: Variable Importance

raw_predictions, x = tft.predict(val_dataloader, mode="raw", return_x=True)
interpretation = tft.interpret_output(raw_predictions, reduction="sum")
fig = tft.plot_interpretation(interpretation)

Example result: Variable importance shows that momentum_12_1 (0.22), vix (0.18) and days_to_earnings (0.15) are main predictors. short_interest_ratio (0.04) — insignificant.

Attention pattern visualization: model pays maximum attention to points 5 and 20 days before forecast — corresponds to weekly and monthly momentum effect.

Benchmark Against Other Methods

On M5 competition (Walmart demand forecasting, 2020):

• TFT: RMSSE 0.1127 (top-10%)
• LightGBM: 0.1152
• DeepAR: 0.1189
• Prophet: 0.1402

TFT advantage is especially pronounced with known future covariates and static features.

Timeline: TFT baseline for 50+ instruments — 4-5 weeks. Full system with earnings calendar, macro covariates and portfolio construction — 3-4 months.