forecasting

functime supports both individual forecasters and forecasters with automated lags / hyperparameter tuning. Auto-forecasters uses FLAML to optimize both hyperparameters and number of lagged dependent variables. FLAML is a SOTA library for automated hyperparameter tuning using the CFO (Frugal Optimization for Cost-related Hyperparameters¹) algorithm. All individual forecasters (e.g. lasso / xgboost) and automated forecasters (e.g. auto_lasso and auto_xgboost) implement the following API.

`forecaster`

Autoregressive forecaster.

Parameters:

Name	Type	Description	Default
`freq`	`str`	Offset alias supported by Polars.	required
`lags`	`int`	Number of lagged target variables.	required
`max_horizons`	`Optional[int]`	Maximum number of horizons to predict directly. Only applied if `strategy` equals "direct" or "ensemble".	`None`
`strategy`	`Optional[str]`	Forecasting strategy. Currently supports "recursive", "direct", and "ensemble" of both recursive and direct strategies.	`None`
`target_transform`	`Optional[Transformer]`	functime transformer to apply to `y` before fit. The transform is inverted at predict time.	`None`
`feature_transform`	`Optional[Transformer]`	functime transformer to apply to `X` before fit and predict.	`None`
`**kwargs`	`Mapping[str, Any]`	Additional keyword arguments passed into underlying sklearn-compatible regressor.	`{}`

`auto_forecaster`

Forecaster with automated hyperparameter tuning and lags selection.

Parameters:

Name	Type	Description	Default
`freq`	`str`	Offset alias as dictated.	required
`min_lags`	`int`	Minimum number of lagged target values.	`3`
`max_lags`	`int`	Maximum number of lagged target values.	`12`
`max_horizons`	`Optional[int]`	Maximum number of horizons to predict directly. Only applied if `strategy` equals "direct" or "ensemble".	`None`
`strategy`	`Optional[str]`	Forecasting strategy. Currently supports "recursive", "direct", and "ensemble" of both recursive and direct strategies.	`None`
`test_size`	`int`	Number of lags.	`1`
`step_size`	`int`	Step size between backtest windows.	`1`
`n_splits`	`int`	Number of backtest splits.	`5`
`time_budget`	`int`	Maximum time budgeted to train each forecaster per window and set of hyperparameters.	`5`
`search_space`	`Optional[dict]`	Equivalent to `config` in FLAML	`None`
`points_to_evaluate`	`Optional[dict]`	Equivalent to `points_to_evaluate` in FLAML	`None`
`num_samples`	`int`	Number of hyper-parameter sets to test. -1 means unlimited (until `time_budget` is exhausted.)	`-1`
`target_transform`	`Optional[Transformer]`	functime transformer to apply to `y` before fit. The transform is inverted at predict time.	`None`
`feature_transform`	`Optional[Transformer]`	functime transformer to apply to `X` before fit and predict.	`None`
`**kwargs`	`Mapping[str, Any]`	Additional keyword arguments passed into underlying sklearn-compatible regressor.	`{}`

`ann`

Autoregressive approximate nearest neighbors built on Lance.

`auto_elastic_net`

ElasticNet forecaster with automated lags and hyperparameters selection.

`auto_knn`

KNN forecaster with automated lags and hyperparamters selection.

`auto_lasso`

LASSO forecaster with automated lags and hyperparameters selection.

`auto_lightgbm`

LightGBM forecaster with automated lags and hyperparamters selection.

`auto_linear_model`

Autoregressive linear forecaster with automated lags selection.

`auto_ridge`

Ridge forecaster with automated lags and hyperparameters selection.

`censored_model`

Censored forecaster given threshold parameter (defaults to 0.0).

Two separate forecasters are fit above and below a certain threshold. The forecasts are then combined using a binary classifier where: {0: "below_threshold", 1: "above_threshold"}.

`elastic_net`

Autoregressive ElasticNet forecaster.

`elastic_net_cv`

Autoregressive ElasticNetCV forecaster.

`flaml_lightgbm`

Autoregressive FLAML LightGBM forecaster with automated lags and hyperparameter tuning.

`lasso`

Autoregressive LASSO forecaster.

`lasso_cv`

Autoregressive LassoCV forecaster.

`linear_model`

Autoregressive linear forecaster.

`ridge`

Autoregressive Ridge forecaster.

`ridge_cv`

Autoregressive RidgeCV forecaster.

`zero_inflated_model`

Censored forecaster with threshold at 0.

https://arxiv.org/abs/2005.01571 ↩