# Analyse and Interpret Time Series Features

## Documentation for package ‘theftdlc’ version 0.2.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

calculate_interval | Calculate interval summaries with a measure of central tendency of classification results
classify | 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 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
plot.interval_calculations | Produce a plot for a interval_calculations object
project | Project a feature matrix into a two-dimensional representation using PCA, MDS, t-SNE, or UMAP ready for plotting
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
shrink | Use a cross validated penalized maximum likelihood generalized linear model to perform feature selection
stat_test | Calculate p-values for feature sets or features relative to an empirical null or each other using resampled t-tests
theftdlc | Analyse and Interpret Time Series Features
tsfeature_classifier | Fit classifiers using time-series features using a resample-based approach and get a fast understanding of performance