Function reference • theftdlc

All functions

classify() tsfeature_classifier(): Fit classifiers using time-series features using a resample-based approach and get a fast understanding of performance

cluster(): Perform cluster analysis of time series using their feature vectors

compare_features(): Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets

filter_duplicates(): Remove duplicate features that exist in multiple feature sets and retain a reproducible random selection of one of them

filter_good_features(): Filter resample data sets according to good feature list

find_good_features(): Helper function to find features in both train and test set that are "good"

fit_models(): Fit classification model and compute key metrics

get_rescale_vals(): Calculate central tendency and spread values for all numeric columns in a dataset

interval() calculate_interval(): Calculate interval summaries with a measure of central tendency of classification results

make_title(): Helper function for converting to title case

plot(<feature_calculations>): Produce a plot for a feature_calculations object

plot(<feature_projection>): Produce a plot for a feature_projection object

project() reduce_dims(): Project a feature matrix into a two-dimensional representation using PCA, MDS, t-SNE, or UMAP ready for plotting

resample_data(): Helper function to create a resampled dataset

rescale_zscore(): Calculate z-score for all columns in a dataset using train set central tendency and spread

select_stat_cols(): Helper function to select only the relevant columns for statistical testing

stat_test(): Calculate p-values for feature sets or features relative to an empirical null or each other using resampled t-tests

theftdlc theftdlc-package: Analyse and Interpret Time Series Features