Queues a forecast job and returns immediately with its job_id. The job runs in a sandbox; poll GET /v2/forecast/jobs/{job_id} for its state and result. Accepts the same body as the synchronous endpoint plus an optional job_options.
Model to use as a string. Common options are (but not restricted to) timegpt-1 and timegpt-1-long-horizon. Full options vary by different users. Contact support@nixtla.io for more information. We recommend using timegpt-1-long-horizon for forecasting if you want to predict more than one seasonal period given the frequency of your data.
A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.
A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals.
The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.
The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps is larger than 0. Default is a robust loss function that is less sensitive to outliers.
The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models; it has no effect on the other models. By default, the value is set to 1.
Method used to compute feature contributions. Options are: 'shapley' (default), 'intervention', 'granger', 'transfer_entropy'. The methods differ in semantics: 'shapley' returns per-timestep contributions that sum to the forecast (last row = per-timestep base prediction); 'intervention' returns each feature's counterfactual effect (forecast minus forecast with that feature held at its baseline) and does NOT sum to the forecast; 'granger'/'transfer_entropy' allocate each series' forecast deviation from its mean proportionally to model-agnostic historical importance weights — the rows sum to the forecast by construction, but they are a proportional allocation describing relationships in the data, not a per-feature attribution of this specific forecast. Use the /v2/explain endpoint for standalone historical importance weights.
Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.
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