# Feature Descriptions The *catch22* feature set provides open access to a powerful set of time-series analysis features that implemented efficiently for fast native feature computation in multiple coding languages. But the features (taken from the larger *hctsa* time-series feature set) can be hard to interpret :disappointed\_relieved: The material in this chapter should help you to understand what each of these 22 features is doing, and point you to the relevant time-series theory that underpins it. ## *catch22* Feature Descriptions Select the feature overview table for a high level description of each feature, or delve into detailed explanations and examples by exploring each of the 9 categories of time-series features included in *catch22*:


Feature overview table	feature-overview-table	table_of_spis_light.png
Distribution shape	distribution-shape	dist_shape_light.png
Extreme event timing	extreme-event-timing	extreme_event_light.png
Linear autocorrelation structure	linear-autocorrelation-structure	linear_autocorr_light.png
Nonlinear autocorrelation	nonlinear-autocorrelation	non_linear_autocorr_icon.png
Symbolic	symbolic	symbolic_light.png
Incremental differences	incremental-differences	incremental_diff_light.png
Simple forecasting	simple-forecasting	forecasting_light.png
Self-affine scaling	self-affine-scaling	self_affine_light.png
Other	other	other_light.png

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