ARIMA

Model

DashAI.back.models.forecasting.ARIMA

Model a series as its own past values plus its own past errors.

ARIMA combines three ideas, one per order. The autoregressive part (p) makes the forecast a weighted sum of recent values. The differencing part (d) subtracts consecutive observations until what is left has no trend, which is what lets the other two parts assume a stable level. The moving average part (q) lets recent forecast errors feed back in.

It is the standard reference model for a series without a strong seasonal pattern. For seasonality, prefer :class:ExponentialSmoothing with a seasonal component, since seasonal ARIMA would need three more orders that this wrapper does not expose.

The orders are not chosen automatically. d = 1 suits a series that trends, d = 0 one that hovers around a level, and the DashAI optimizer can search all three when told to.

References

Parameters

p : integer, default=1: How many past values the forecast is a weighted sum of, the autoregressive order.
d : integer, default=1: How many times to difference the series before modelling it. 1 removes a straight trend, 0 suits a series that already hovers around a fixed level.
q : integer, default=0: How many past forecast errors feed into the next forecast, the moving average order.

Methods

predict(self, x: 'DashAIDataset') -> 'np.ndarray'

Defined on ARIMA

Forecast forward from the end of the training series.

Parameters

x : DashAIDataset: The rows to forecast, whose dates say how far ahead each one is.

Returns

np.ndarray: One forecast value per requested row.

train(self, x_train: 'DashAIDataset', y_train: 'DashAIDataset', x_validation: 'DashAIDataset' = None, y_validation: 'DashAIDataset' = None) -> 'ARIMA'

Defined on ARIMA

Fit the ARIMA model to the series.

Parameters

x_train : DashAIDataset: The date column. statsmodels is given the values in order and the spacing is assumed regular; the dates are kept so a later partition can be forecast at its own dates.
y_train : DashAIDataset: The series to forecast.
x_validation : DashAIDataset, optional: Unused.
y_validation : DashAIDataset, optional: Unused.

Returns

ARIMA: The fitted model.

calculate_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.VALIDATION: 'validation'>, level: DashAI.back.core.enums.metrics.LevelEnum = <LevelEnum.LAST: 'last'>, log_index: int = None, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None, fold_index: int = None, inner_fold_index: int = None)

Defined on BaseModel

Calculate and save metrics for a given data split and level.

Parameters

split : SplitEnum: The data split to evaluate (TRAIN, VALIDATION, or TEST). Defaults to SplitEnum.VALIDATION.
level : LevelEnum: The metric granularity level (LAST, TRIAL, STEP, or BATCH). Defaults to LevelEnum.LAST.
log_index : int, optional: Explicit step index for the metric entry. If None, the next step index is computed automatically. Defaults to None.
x_data : DashAIDataset, optional: Input features. If None, the dataset stored in the model for the given split is used. Defaults to None.
y_data : DashAIDataset, optional: Target labels. If None, the labels stored in the model for the given split are used. Defaults to None.

compute_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.TEST: 'test'>, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None) -> Dict[str, float]

Defined on BaseModel

Calculate and return metric scores for a given data split.

Parameters

split : SplitEnum: The data split to evaluate (TRAIN, VALIDATION, or TEST). Defaults to SplitEnum.VALIDATION.
x_data : DashAIDataset, optional: Input features. If None, the dataset stored in the model for the given split is used. Defaults to None.
y_data : DashAIDataset, optional: Target labels. If None, the labels stored in the model for the given split are used. Defaults to None.

Returns

Dict[str, float]: A dictionary mapping metric names to their computed scores.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str: Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential: An instance of the requested credential component.

get_metadata(cls) -> Dict[str, Any]

Defined on BaseModel

Get metadata values for the current model.

Returns

Dict[str, Any]: Dictionary containing UI metadata such as the model icon used in the DashAI frontend.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict: Dictionary representing the Json Schema of the component.

load(filename: str) -> Any

Defined on ForecastingModel

Deserialise a model from disk using joblib.

Parameters

filename : str: Path to the file previously written by :meth:save.

Returns

ForecastingModel: The loaded model instance.

predict_prepared(self, features: Any) -> Any

Defined on BaseModel

Predict from data that is already in this model's feature space.

Parameters

features : pandas.DataFrame or numpy.ndarray: Feature matrix as returned by prepare_dataset(..., is_fit=False). No further preparation is applied to it.

Returns

Any: The same kind of output as predict: predicted values for regressors, class probabilities for DashAI classifiers.

predict_proba_prepared(self, features: Any) -> Any

Defined on BaseModel

Return class probabilities for data already in the feature space.

Parameters

features : pandas.DataFrame or numpy.ndarray: Feature matrix as returned by prepare_dataset(..., is_fit=False).

Returns

numpy.ndarray: Array of shape (n_samples, n_classes) with class probabilities.

prepare_dataset(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on BaseModel

Hook for model specific preprocessing of input features.

Parameters

dataset : DashAIDataset: The input dataset to preprocess.
is_fit : bool: Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset: The preprocessed dataset ready to be fed into the model.

prepare_output(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on BaseModel

Hook for model-specific preprocessing of output targets.

Parameters

dataset : DashAIDataset: The output dataset (target labels) to preprocess.
is_fit : bool: Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset: The preprocessed output dataset.

save(self, filename: str) -> None

Defined on ForecastingModel

Serialise the model to disk using joblib.

Parameters

filename : str: Destination file path where the model will be written.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict: A dictionary with the data provided by the user to initialize the model.

Returns

dict: A validated dictionary with the necessary objects.

Compatible with

ForecastingTask

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Compatible with​

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Compatible with