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 |
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(period, …) |
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, country, …) |
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. |