_create_holidays_df(holidays, index, as_is)

_create_holidays_df_dataframe(holidays, ...)

_create_holidays_df_str(holidays, index, as_is)

_get_borders_ts(ts, start, end)

Get start and end parameters according to given TSDataset.


Get quantiles that are present inside the TSDataset.

_get_fictitious_relevances(pvalues, alpha)

Convert p-values into fictitious variables, with function f(x) = 1 - x.

_get_labels_names(trend_transform, segments)

If only unique transform classes are used then show their short names (without parameters).


Prepare dictionary with forecasts results.

_select_quantiles(forecast_results, quantiles)

Select quantiles from the forecast results.


Validate if segments aren't intersecting.

get_correlation_matrix(ts[, columns, ...])

Compute pairwise correlation of timeseries for selected segments.

get_residuals(forecast_df, ts)

Get residuals for further analysis.

metric_per_segment_distribution_plot(...[, ...])

Plot per-segment metrics distribution.

plot_anomalies(ts, anomaly_dict[, ...])

Plot a time series with indicated anomalies.

plot_anomalies_interactive(ts, segment, ...)

Plot a time series with indicated anomalies.

plot_backtest(forecast_df, ts[, segments, ...])

Plot targets and forecast for backtest pipeline.

plot_backtest_interactive(forecast_df, ts[, ...])

Plot targets and forecast for backtest pipeline using plotly.

plot_change_points_interactive(ts, ...[, ...])

Plot a time series with indicated change points.

plot_clusters(ts, segment2cluster[, ...])

Plot clusters [with centroids].

plot_correlation_matrix(ts[, columns, ...])

Plot pairwise correlation heatmap for selected segments.

plot_feature_relevance(ts, relevance_table)

Plot relevance of the features.

plot_forecast(forecast_ts[, test_ts, ...])

Plot of prediction for forecast pipeline.

plot_forecast_decomposition(forecast_ts[, ...])

Plot of prediction and its components.

plot_holidays(ts, holidays[, segments, ...])

Plot holidays for segments.

plot_imputation(ts, imputer[, segments, ...])

Plot the result of imputation by a given imputer.

plot_metric_per_segment(metrics_df, metric_name)

Plot barplot with per-segment metrics.

plot_periodogram(ts, period[, ...])

Plot the periodogram using scipy.signal.periodogram().

plot_residuals(forecast_df, ts[, feature, ...])

Plot residuals for predictions from backtest against some feature.

plot_time_series_with_change_points(ts, ...)

Plot segments with their trend change points.

plot_trend(ts, trend_transform[, segments, ...])

Plot series and trend from trend transform for this series.



Enum for components plotting modes.


Enum for types of plot in metric_per_segment_distribution_plot().


Enum for types of aggregation in a metric per-segment plot.