Understanding the Output and ForecastConfig

How to read the point forecast and quantile bands, how to derive prediction intervals at a chosen confidence level, and every ForecastConfig field with its effect.

📊 Understanding the Output

Quantile Forecast Structure

TimesFM returns (point_forecast, quantile_forecast):

point_forecast: shape (batch, horizon) — the median (0.5 quantile)
quantile_forecast: shape (batch, horizon, 10) — ten slices:

Index	Quantile	Use
0	Mean	Average prediction
1	0.1	Lower bound of 80% PI
2	0.2	Lower bound of 60% PI
3	0.3	—
4	0.4	—
5	0.5	Median (= `point_forecast`)
6	0.6	—
7	0.7	—
8	0.8	Upper bound of 60% PI
9	0.9	Upper bound of 80% PI

Extracting Prediction Intervals

python

point, q = model.forecast(horizon=H, inputs=data)

# 80% prediction interval (most common)
lower_80 = q[:, :, 1]  # 10th percentile
upper_80 = q[:, :, 9]  # 90th percentile

# 60% prediction interval (tighter)
lower_60 = q[:, :, 2]  # 20th percentile
upper_60 = q[:, :, 8]  # 80th percentile

# Median (same as point forecast)
median = q[:, :, 5]

mermaid

flowchart LR
    accTitle: Quantile Forecast Anatomy
    accDescr: Diagram showing how the 10-element quantile vector maps to prediction intervals.

    input["📈 Input Series
1-D array"] --> model["🤖 TimesFM
compile + forecast"]
    model --> point["📍 Point Forecast
(batch, horizon)"]
    model --> quant["📊 Quantile Forecast
(batch, horizon, 10)"]
    quant --> pi80["80% PI
q[:,:,1] – q[:,:,9]"]
    quant --> pi60["60% PI
q[:,:,2] – q[:,:,8]"]
    quant --> median["Median
q[:,:,5]"]

    classDef data fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#1e3a5f
    classDef model fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#581c87
    classDef output fill:#dcfce7,stroke:#16a34a,stroke-width:2px,color:#14532d

    class input data
    class model model
    class point,quant,pi80,pi60,median output

🔧 ForecastConfig Reference

All forecasting behavior is controlled by timesfm.ForecastConfig:

python

timesfm.ForecastConfig(
    max_context=1024,                    # Max context window (truncates longer series)
    max_horizon=256,                     # Max forecast horizon
    normalize_inputs=True,               # Normalize inputs (RECOMMENDED for stability)
    per_core_batch_size=32,              # Batch size per device (tune for memory)
    use_continuous_quantile_head=True,   # Better quantile accuracy for long horizons
    force_flip_invariance=True,          # Ensures f(-x) = -f(x) (mathematical consistency)
    infer_is_positive=True,              # Clamp forecasts ≥ 0 when all inputs > 0
    fix_quantile_crossing=True,          # Ensure q10 ≤ q20 ≤ ... ≤ q90
    return_backcast=False,               # Return backcast (for covariate workflows)
)

Parameter	Default	When to Change
`max_context`	0	Set to match your longest historical window (e.g., 512, 1024, 4096)
`max_horizon`	0	Set to your maximum forecast length
`normalize_inputs`	False	Always set True — prevents scale-dependent instability
`per_core_batch_size`	1	Increase for throughput; decrease if OOM
`use_continuous_quantile_head`	False	Set True for calibrated prediction intervals
`force_flip_invariance`	True	Keep True unless profiling shows it hurts
`infer_is_positive`	True	Set False for series that can be negative (temperature, returns)
`fix_quantile_crossing`	False	Set True to guarantee monotonic quantiles