Analysis

API details

See the API documentation for further details on available analysis tools:

Decomposition analysis utilities:

find_change_points(ts, in_column, ...)	Find trend change points using ruptures models.
plot_change_points_interactive(ts, ...[, ...])	Plot a time series with indicated change points.
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.
seasonal_plot(ts[, freq, cycle, alignment, ...])	Plot each season on one canvas for each segment.
stl_plot(ts, period[, segments, ...])	Plot STL decomposition for segments.

SeasonalPlotAggregation(value)	Enum for types of aggregation in a seasonal plot.
SeasonalPlotAlignment(value)	Enum for types of alignment in a seasonal plot.
SeasonalPlotCycle(value)	Enum for types of cycles in a seasonal plot.

EDA utilities:

acf_plot(ts[, n_segments, lags, partial, ...])	Autocorrelation and partial autocorrelation plot for multiple timeseries.
cross_corr_plot(ts[, n_segments, maxlags, ...])	Cross-correlation plot between multiple timeseries.
distribution_plot(ts[, n_segments, ...])	Distribution of z-values grouped by segments and time frequency.
get_correlation_matrix(ts[, columns, ...])	Compute pairwise correlation of timeseries for selected segments.
plot_clusters(ts, segment2cluster[, ...])	Plot clusters [with centroids].
plot_correlation_matrix(ts[, columns, ...])	Plot pairwise correlation heatmap for selected segments.
plot_holidays(ts, holidays[, segments, ...])	Plot holidays for segments.
plot_imputation(ts, imputer[, segments, ...])	Plot the result of imputation by a given imputer.
plot_periodogram(ts, period[, ...])	Plot the periodogram using scipy.signal.periodogram().

Feature selection analysis utilities:

plot_feature_relevance(ts, relevance_table)	Plot relevance of the features.
ModelRelevanceTable()	ModelRelevanceTable builds feature relevance table using feature relevance values obtained from model.
RelevanceTable(greater_is_better)	Abstract class for relevance table computation.
StatisticsRelevanceTable()	StatisticsRelevanceTable builds feature relevance table with tsfresh statistics.
get_model_relevance_table(df, df_exog, model)	Calculate relevance table with feature importance from model.
get_statistics_relevance_table(df, df_exog)	Calculate relevance table with p-values from tsfresh.
AggregationMode(value)	Enum for different aggregation modes.

Forecast analysis utilities:

get_residuals(forecast_df, ts)	Get residuals for further analysis.
metric_per_segment_distribution_plot(...[, ...])	Plot per-segment metrics distribution.
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_forecast(forecast_ts[, test_ts, ...])	Plot of prediction for forecast pipeline.
plot_forecast_decomposition(forecast_ts[, ...])	Plot of prediction and its components.
plot_metric_per_segment(metrics_df, metric_name)	Plot barplot with per-segment metrics.
plot_residuals(forecast_df, ts[, feature, ...])	Plot residuals for predictions from backtest against some feature.
prediction_actual_scatter_plot(forecast_df, ts)	Plot scatter plot with forecasted/actual values for segments.
qq_plot(residuals_ts[, qq_plot_params, ...])	Plot Q-Q plots for segments.

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.

Outliers analysis utilities:

plot_anomalies(ts, anomaly_dict[, ...])	Plot a time series with indicated anomalies.
plot_anomalies_interactive(ts, segment, ...)	Plot a time series with indicated anomalies.
get_anomalies_density(ts[, in_column, ...])	Compute outliers according to density rule.
get_anomalies_hist(ts[, in_column, ...])	Get point outliers in time series using histogram model.
get_anomalies_median(ts[, in_column, ...])	Get point outliers in time series using median model (estimation model-based method).
get_anomalies_prediction_interval(ts, model)	Get point outliers in time series using prediction intervals (estimation model-based method).
get_anomalies_iqr(ts[, in_column, ...])	Get point outliers in time series using IQR statistics, estimated on a rolling window.
get_anomalies_isolation_forest(ts[, ...])	Get point outliers in time series using Isolation Forest algorithm.
get_anomalies_mad(ts[, in_column, ...])	Get point outliers in time series using median absolute deviation.