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classify()
tsfeature_classifier()
- Fit classifiers using time-series features using a resample-based approach and get a fast understanding of performance
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cluster()
- Perform cluster analysis of time series using their feature vectors
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compare_features()
- Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets
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filter_duplicates()
- Remove duplicate features that exist in multiple feature sets and retain a reproducible random selection of one of them
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filter_good_features()
- Filter resample data sets according to good feature list
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find_good_features()
- Helper function to find features in both train and test set that are "good"
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fit_models()
- Fit classification model and compute key metrics
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get_rescale_vals()
- Calculate central tendency and spread values for all numeric columns in a dataset
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interval()
calculate_interval()
- Calculate interval summaries with a measure of central tendency of classification results
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make_title()
- Helper function for converting to title case
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plot(<feature_calculations>)
- Produce a plot for a feature_calculations object
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plot(<feature_projection>)
- Produce a plot for a feature_projection object
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project()
reduce_dims()
- Project a feature matrix into a two-dimensional representation using PCA, MDS, t-SNE, or UMAP ready for plotting
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resample_data()
- Helper function to create a resampled dataset
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rescale_zscore()
- Calculate z-score for all columns in a dataset using train set central tendency and spread
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select_stat_cols()
- Helper function to select only the relevant columns for statistical testing
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stat_test()
- Calculate p-values for feature sets or features relative to an empirical null or each other using resampled t-tests
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theftdlc
theftdlc-package
- Analyse and Interpret Time Series Features