Analyse and Interpret Time Series Features

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Documentation for package ‘theftdlc’ version 0.1.0

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theftdlc-package Analyse and Interpret Time Series Features
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
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
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