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tw.forecasting

The Forecasting class provides a comprehensive suite of methods for forecasting time-series data. It supports operations like model building, prediction generation, root cause analysis (RCA), and automatic forecasting, making it an essential tool for advanced data-driven forecasting tasks.

Class Initialization

PY
from tangent_works import TangentWorks

# Initialize the TangentWorks instance
tw = TangentWorks()

# Access the Forecasting class
tw.forecasting

Methods

build_model

tangent_works.forecasting.build_model(configuration=Dict[str, Any], dataset=pd.DataFrame)

Builds a forecasting model using the provided configuration and dataset.

Parameters

  • configuration (Dict[str, Any]): The configuration parameters for model building.

  • dataset (pd.DataFrame): The time-series dataset used to train the model.

Returns

  • ForecastingModel: The constructed forecasting model.

Examples

PY
model = tw.forecasting.build_model(
  configuration = config,
  dataset = data
  )

predict

tangent_works.forecasting.predict(configuration=Dict[str, Any], dataset=pd.DataFrame, model=ForecastingModel)

Generates predictions using the provided configuration, dataset, and pre-trained forecasting model.

Parameters

  • configuration (Dict[str, Any]): The configuration parameters for prediction.

  • dataset (pd.DataFrame): The input time-series data for generating predictions.

  • model (ForecastingModel): A previously built forecasting model.

Returns

  • pd.DataFrame: The generated predictions.

Examples

PY
predictions = tw.forecasting.predict(
  configuration = config,
  dataset = data,
  model = model
  )

rca

tangent_works.forecasting.rca(configuration=Dict[str, Any], dataset=pd.DataFrame, model=ForecastingModel)

Performs root cause analysis (RCA) on the dataset to identify potential causes of anomalies or variations.

Parameters

  • configuration (Dict[str, Any]): Configuration parameters for performing RCA.

  • dataset (pd.DataFrame): The time-series dataset to analyze.

  • model (ForecastingModel): The forecasting model used for RCA.

Returns

  • Dict[int, pd.DataFrame]: A dictionary mapping identified root causes to their corresponding model indexes.

Examples

PY
rca_results = tw.forecasting.rca(
  configuration = config,
  dataset = data,
  model = model
  )

auto_forecast

tangent_works.forecasting.auto_forecast(configuration=Dict[str, Any], dataset=pd.DataFrame)

Automatically builds a forecasting model and generates predictions in a single step.

Parameters

  • configuration (Dict[str, Any]): Configuration parameters for auto-forecasting.

  • dataset (pd.DataFrame): The input time-series dataset to analyze and forecast.

Returns

  • AutoForecastingResult: Contains the built model and predictions.

Examples

PY
auto_result = tw.forecasting.auto_forecast(
  configuration = config,
  dataset = data
  )

Key Features

  • Modular Design: Separate methods for building models, generating predictions, and RCA.

  • Automation: auto_forecast combines model building and prediction into a single step.

  • Flexibility: Accepts various configurations for custom forecasting workflows.


Dependencies

  • pandas: Used for handling time-series data.

  • Tangent Works core and business logic modules: Used for model building, validation, and forecasting.

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