Welcome to giotto-time’s API reference!

gtime.causality: Causality Tests

The gtime.causality module deals with the causality tests for time series data.

causality.ShiftedLinearCoefficient([…])

Test the shifted linear fit coefficients between two or more time series.

causality.ShiftedPearsonCorrelation([…])

Class responsible for assessing the shifted Pearson correlations (PPMCC) between two or more series.

gtime.compose: Compose

The gtime.compose module contains meta-estimators for building composite models with transformers.

compose.FeatureCreation(transformers[, …])

Applies transformers to columns of a pandas DataFrame.

gtime.feature_extraction: Feature Extraction

The gtime.feature_extraction module deals with the creation of features starting from a time series.

feature_extraction.Shift([shift])

Perform a shift of a DataFrame of size equal to shift.

feature_extraction.MovingAverage([window_size])

For each row in time_series, compute the moving average of the previous window_size rows.

feature_extraction.MovingCustomFunction(…)

For each row in time_series, compute the moving custom function of the previous window_size rows.

feature_extraction.Polynomial([degree])

Compute the polynomial feature_extraction, of a degree equal to the input degree.

feature_extraction.Exogenous(…[, method])

Reindex exogenous_time_series with the index of time_series.

feature_extraction.CustomFeature(func, **kwargs)

Constructs a transformer from an arbitrary callable.

gtime.feature_generation: Feature Generation

The gtime.feature_generation module deals with the creation of features that do not depend on the input data, but just on its index.

feature_generation.PeriodicSeasonal([…])

Create a sinusoid from a given date and with a given period and amplitude.

feature_generation.Constant([constant, length])

Generate a pd.DataFrame with one column, of the same length as the input X and containing the value constant across the whole column.

feature_generation.Calendar([region, …])

Create a feature based on the national holidays of a specific country.

gtime.forecasting: Forecasting

The gtime.forecasting module contains a collection of machine learning models, for dealing with time series data.

forecasting.GAR(estimator[, n_jobs])

Generalized Auto Regression model.

forecasting.GARFF(estimator)

Generalized Auto Regression model with feedforward training.

forecasting.TrendForecaster(trend, trend_x0)

Trend forecasting model.

gtime.regressors: Regressors

The gtime.regressors module contains regression models.

regressors.LinearRegressor([loss])

Implementation of a LinearRegressor that takes a custom loss function.

gtime.metrics: Metrics

The gtime.metrics module contains a collection of different metrics.

metrics.smape(y_true, List, numpy.ndarray], …)

Compute the ‘Symmetric Mean Absolute Percentage Error’ (SMAPE) between two vectors.

metrics.max_error(y_true, List, …)

Compute the maximum error between two vectors.

gtime.model_selection: Model Selection

The gtime.model_selection module deals with model selection.

model_selection.FeatureSplitter([drop_na_mode])

Splits the feature matrices X and y in X_train, y_train, X_test, y_test.

model_selection.horizon_shift(time_series, …)

Perform a shift of the original time_series for each time step between 1 and horizon.

gtime.preprocessing: Preprocessing

The gtime.preprocessing module deals with the preprocessing of time series data.

preprocessing.TimeSeriesPreparation([start, …])

Transforms an array-like sequence in a period-index DataFrame with a single column.