Results

skforecast_ai.schemas.results

Classes:

Name	Description
RenderedScript	Structured representation of a rendered forecasting script.
CodeGenerationResult	Result of the forecast_code workflow.
ForecastResult	Result of the forecast workflow (executes the pipeline end-to-end).
BacktestResult	Result of the backtest workflow.
AskResult	Result of the ask workflow (requires LLM).

Classes

RenderedScript

Bases: BaseModel

Structured representation of a rendered forecasting script.

Splits the rendered script into logical sections so that forecast() can exec the core logic while forecast_code() returns the full standalone script.

Attributes:

Name	Type	Description
imports	str	Import statements required by the script.
data_loading	str	Code that loads data from CSV and sets up the index.
core	str	Core execution logic (preprocessing, split, fit, predict, metrics). Operates on a pre-existing data DataFrame variable.

Attributes

imports instance-attribute

imports

data_loading instance-attribute

data_loading

core instance-attribute

core

full_script property

full_script

Return the complete standalone script (imports + loading + core).

executable property

executable

Return code suitable for exec() (imports + core, no CSV loading).

CodeGenerationResult

Bases: DisplayMixin, BaseModel

Result of the forecast_code workflow.

Attributes:

Name	Type	Description
profile	ForecastingProfile	Profile of the input dataset and high-level modeling decisions.
plan	ForecastPlan	Detailed forecasting plan.
code	str	Generated Python script.

Attributes

profile instance-attribute

profile

plan instance-attribute

plan

code instance-attribute

code

Functions

_rich_body

_rich_body(console, options)

Source code in skforecast_ai/schemas/results.py

def _rich_body(
    self, console: Console, options: ConsoleOptions
) -> RenderResult:
    yield render_profile(self.profile)
    yield render_plan(self.plan)

ForecastResult

Bases: DisplayMixin, BaseModel

Result of the forecast workflow (executes the pipeline end-to-end).

Attributes:

Name	Type	Description
profile	ForecastingProfile	Profile of the input dataset and high-level modeling decisions.
plan	ForecastPlan	Detailed forecasting plan that was executed.
code	str	Generated Python script equivalent to the execution.
metrics	pandas DataFrame, None	Evaluation metrics. DataFrame with columns ['series', 'MAE', 'MSE', 'MASE']. For single-series tasks this contains one row; for multi-series tasks one row per level. None in prediction mode (test_size=None), where there is no ground truth to evaluate against.
predictions	pandas DataFrame	Forecasted values for the requested steps. When prediction intervals (or quantiles) are requested, the corresponding bound columns are included alongside the point predictions.

Attributes

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

profile instance-attribute

profile

plan instance-attribute

plan

code instance-attribute

code

metrics instance-attribute

metrics

predictions instance-attribute

predictions

Functions

_rich_body

_rich_body(console, options)

Source code in skforecast_ai/schemas/results.py

def _rich_body(
    self, console: Console, options: ConsoleOptions
) -> RenderResult:
    yield render_profile(self.profile)
    yield render_plan(self.plan)
    if self.metrics is not None:
        yield render_metrics(self.metrics, title="Forecast Metrics")
    yield render_dataframe(self.predictions, title="Predictions")

BacktestResult

Bases: DisplayMixin, BaseModel

Result of the backtest workflow.

Attributes:

Name	Type	Description
profile	ForecastingProfile	Profile of the input dataset and high-level modeling decisions.
plan	ForecastPlan	Detailed forecasting plan that was executed.
cv_config	dict	Resolved TimeSeriesFold parameters for traceability.
metrics	pandas DataFrame	Backtesting metric values returned by skforecast.
predictions	pandas DataFrame	Full backtest predictions across all folds.
code	str	Generated Python script reproducing the backtesting workflow.
explanation	str	Human-readable explanation of the backtesting configuration and results summary.

Attributes

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

profile instance-attribute

profile

plan instance-attribute

plan

cv_config instance-attribute

cv_config

metrics instance-attribute

metrics

predictions instance-attribute

predictions

code instance-attribute

code

explanation instance-attribute

explanation

Functions

_rich_body

_rich_body(console, options)

Source code in skforecast_ai/schemas/results.py

def _rich_body(
    self, console: Console, options: ConsoleOptions
) -> RenderResult:
    yield render_explanation(self.explanation)
    yield render_cv_config(self.cv_config)
    yield render_metrics(self.metrics, title="Backtest Metrics")
    yield render_dataframe(self.predictions, title="Backtest Predictions")
    yield render_profile(self.profile)
    yield render_plan(self.plan)

AskResult

Bases: DisplayMixin, BaseModel

Result of the ask workflow (requires LLM).

Attributes:

Name	Type	Description
profile	ForecastingProfile, default None	Profile of the input dataset and high-level modeling decisions, if data was provided.
plan	ForecastPlan, default None	Detailed forecasting plan, if the agent produced one.
code	str, default None	Generated Python script, if the agent produced one.
explanation	str	LLM-generated explanation or response.

Attributes

profile class-attribute instance-attribute

profile = None

plan class-attribute instance-attribute

plan = None

code class-attribute instance-attribute

code = None

explanation instance-attribute

explanation

Functions

_rich_body

_rich_body(console, options)

Source code in skforecast_ai/schemas/results.py

def _rich_body(
    self, console: Console, options: ConsoleOptions
) -> RenderResult:
    yield render_explanation(self.explanation, title="Assistant Response")

Functions