plotters

Functions

_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_existing_quantiles(ts)	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_forecast_results(forecast_ts)	Prepare dictionary with forecasts results.
_select_quantiles(forecast_results, quantiles)	Select quantiles from the forecast results.
_validate_intersecting_segments(fold_numbers)	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_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.

Classes

MetricPlotType(value)	Enum for types of plot in metric_per_segment_distribution_plot().
PerFoldAggregation(value)	Enum for types of aggregation in a metric per-segment plot.