TimesFM Forecasting

Choose the model/API first

The Python package version is 3.0.2; checkpoint versions are separate.

Checkpoint	Interface	Quantile output	Usage terms
2.5, 200M	timesfm.TimesFM_2p5_200M_torch, compile, forecast	mean + 9 deciles, median index 5	Apache-2.0 weights; bundled CLI default
3.0, about 330M	timesfm3.TimesFM3Forecaster, predict / predict_batch	9 deciles, median index 4	Downloaded weights restricted to non-commercial, non-production use

Upstream identifies 3.0 as the latest model. This skill retains 2.5 as its default local workflow because its Apache-licensed weights have different usage terms. For 3.0 multivariate targets, past-only covariates or Apple MLX, read references/timesfm3.md before adapting code. Authorized Google Cloud services have separate terms; a Cloud entitlement does not change the license of downloaded weights. See the upstream license notice.

Use TimesFM for forecasting an ordered temporal target without task-specific model training. It is not a causal effect estimator, clinical detector, or physics-based climate model. Zero-shot describes fitting; it does not establish absence of benchmark overlap in pretraining or good accuracy in a new domain.

Workflow

1. Establish cadence, units, forecast origin, horizon, known-at-origin covariates, evaluation cutoffs and a naive/seasonal-naive baseline.
2. Validate a sorted, unique, regular time grid. Do not dropna() internal gaps: that changes temporal spacing. Reject nonfinite inputs, or explicitly impute inside each training history. Never fill using held-out future targets.
3. Run python scripts/check_system.py before downloading/loading weights. Its available-RAM and cache-volume thresholds are heuristics, not a guarantee against OOM. Start with batch size 1; measure actual peak usage.
4. Load the chosen checkpoint with a recorded immutable revision. For 2.5, compile explicit positive context/horizon settings; zero does not mean “use the maximum.” Keep patch-rounded context + horizon <=16,384 and horizon <=1,024 when using the continuous quantile head.
5. Forecast, validate shapes/finite values/quantile ordering and export the forecast origin, frequency, configuration and checkpoint revision.
6. Evaluate across rolling origins with preprocessing fitted independently per origin. Report accuracy, interval coverage and interval width by horizon; label quantile intervals nominal until calibrated on relevant data.

Installation

Run in a separate environment. The commands below target the reviewed release. Shell extras and version constraints must be quoted, particularly in zsh.

uv venv .venv-timesfm
uv pip install --python .venv-timesfm/bin/python "timesfm[torch]==3.0.2" numpy pandas
.venv-timesfm/bin/python scripts/check_system.py

Use the current PyTorch installation selector for a CUDA wheel matching the host. The 2.5 loader in this release chooses cuda:0 if CUDA exists, otherwise CPU; detecting MPS does not enable MPS inference. Do not use model.to(...) on the wrapper as if it were an nn.Module. For CPU-only XReg, install jax and scikit-learn alongside the PyTorch profile; the upstream [xreg] extra requests jax[cuda], which is not appropriate for macOS. Flax is a separate optional backend; it is not required by the bundled scripts.

from importlib.metadata import version
import timesfm
print(version("timesfm"))  # package has no guaranteed timesfm.__version__
assert hasattr(timesfm, "TimesFM_2p5_200M_torch")

TimesFM 2.5 quick start

The checkpoint-load examples are illustrative: validation of this refresh used native package code with tiny random models and controlled decode fixtures, without downloading pretrained weights. That verifies mechanics, not accuracy.

import numpy as np
import timesfm

model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
    "google/timesfm-2.5-200m-pytorch",
    revision="1d952420fba87f3c6dee4f240de0f1a0fbc790e3",
    torch_compile=False,  # avoid compilation startup during an initial smoke run
)
model.compile(timesfm.ForecastConfig(
    max_context=512, max_horizon=128, per_core_batch_size=1,
    normalize_inputs=True, use_continuous_quantile_head=True,
    force_flip_invariance=True, infer_is_positive=False,
    fix_quantile_crossing=True,
))
histories = [np.sin(np.linspace(0, 20, 200)).astype(np.float32)]
# 2.5 may append dummy series to its input list during batching; pass a fresh list.
point, q = model.forecast(horizon=24, inputs=list(histories))
assert point.shape == (1, 24) and q.shape == (1, 24, 10)
assert np.isfinite(q).all() and np.all(np.diff(q[..., 1:], axis=-1) >= 0)
assert np.allclose(point, q[..., 5])
lower_80, upper_80 = q[..., 1], q[..., 9]  # q10-q90: nominal 80% PI
lower_60, upper_60 = q[..., 2], q[..., 8]  # q20-q80: nominal 60% PI

Set infer_is_positive=True only for a target domain that is truly nonnegative. A positive observed window does not establish that temperature anomalies, returns or residuals cannot become negative. Monotonic quantiles and a continuous quantile head do not establish interval calibration.

CSV helper

python scripts/forecast_csv.py monthly_sales.csv \
  --date-col date --freq MS --value-cols sales,revenue \
  --horizon 12 --batch-size 1 --nonnegative --output forecasts.csv

forecast_csv.py validates before loading, sorts dates, rejects duplicate headers/dates, checks the complete regular grid and preserves missing positions. Missing values fail by default; --missing interpolate fills only internal gaps, never leading/trailing values. Without --date-col, row order is assumed to be the regular grid and output uses steps. Numeric IDs must be excluded using --value-cols. --max-context controls history truncation; --horizon is limited to 1..1,024 for this quantile-head workflow.

Outputs retain forecast, median, lower_80, upper_80, lower_60, upper_60. A .metadata.json sidecar records origin, cadence, model revision and config. Old skill releases mislabeled q10-q90/q20-q80 as 90%/80%; migrate old outer *_90 to *_80 and old inner *_80 to *_60 simultaneously. Old generated example forecasts were removed because their mapping/provenance was invalid.

Covariates and anomaly screening

For 2.5 XReg, compile return_backcast=True, retain targets and covariates on identical grids and provide dynamic covariates over context and horizon. "xreg + timesfm" fits regression on targets, then forecasts regression residuals. "timesfm + xreg" forecasts first, then fits regression on backcast residuals. The latter needs more than one input patch (32 observations). In package 3.0.2, the implementation returns lists of combined point and quantile forecasts; an inherited docstring incorrectly describes the second return as XReg-only. See references/api_reference.md for the complete call.

Quantile exceedances may screen for unusual observations, but even calibrated 80% intervals exclude about 20% of ordinary observations marginally. They do not supply anomaly probabilities, familywise control or validated alarm severity. Retrospective detrended Z scores also differ from prospective anomaly detection.

References and examples

• API reference: 2.5 loader, compile, forecast and XReg contracts.
• Output/config: all real ForecastConfig fields and interval indexing.
• Data preparation: grid, missingness, covariate timing and format recipes.
• Workflows: held-out and rolling-origin validation, baseline comparisons.
• System requirements: resource checks and backend requirements.
• Performance tuning: measured batch/context tuning.
• Examples and validation: example commands and testing scope.
• TimesFM 3.0: multivariate API, 9-decile layout and usage boundary.

Primary review sources: official source, 3.0.2 distribution, 2.5 model card, 3.0 model card.