Getting started
Some advice for getting started with an hctsa analysis
Preparing for an analysis
hctsa automates some parts of a time-series analysis pipeline (such as guiding the selection of informative statistical properties in a time-series classification problem), but it does not replace expertise. Careful thought, with understanding of the problem and domain-specific issues are essential in designing how hctsa can be used for a given application, and in properly interpreting its results.
Some general advice before embarking on an hctsa analysis:
What are the data?
For long, streaming data: how long is each time series? Does this capture the timescale of the problem you care about?
For continuously evolving processes: At what rate are the data sampled? Does this capture the patterns of interest? E.g., if the sampling rate is too high, this can lead to trivially autocorrelated time series such that time-series methods in hctsa will find it challenging to resolve the patterns of interest.
Are the appropriately processed (detrended, artifacts removed, …)? This requires careful thought, often with domain expertise, about what problem is being solved. E.g., many time-series analysis algorithms will be dominated by underlying trends if underlying trends in time series are not removed. In general, properties of the data, especially if they're likely to affect many time-series analysis algorithms (like underlying trends, artefactual outliers, etc.) should be removed if they're not informative of the differences you care about distinguishing.
What do they look like? Addressing the questions above requires you to look at each of the time series to get a sense of the dynamics you're interested in characterizing using time-series features.
What problem are you trying to solve? In designing an analysis using hctsa, you will need to think about the sample size you have, what effect sizes are expected, what statistical power will you have, etc. E.g., if you only have 5 examples of each of two classes, you will not have the statistical power to pick out individual features from a library of 7000, simple (unregularized) classifiers will be likely to overfit, etc.
Trial run with a reduced set. Once you’ve devised a pipeline, it's best to run through it in hctsa but using a reduced feature set first (e.g., the catch22 set), which runs quickly on a laptop and gives you a sense for the process. Once you're satsified with the analysis pipeline, you can always scale up to the full hctsa library of >7000 features.
Example hctsa Analysis Pipelines
Sometimes it's easiest to quickly get going when you can follow on to some example code. Here are some repositories that allow you to do just this.
EEG
An overview tutorial on applying hctsa to a 5-class EEG dataset is in this GitHub repo (including the use of reduced feature set, catch22, within the hctsa framework). There is also a recording of the tutorial (final ~hour is a hands-on demo using this dataset) on YouTube.
Flies in a tube
You can play with hctsa using this fly movement dataset and analysis code. This repository allows you to skip the process of running an hctsa calculation (you can download pre-computed results), and get straight down to following some analyses, including code for classifying recordings during the day versus night, or from males versus females using time-series feature extraction.
Worms in a dish
You can try your hand at classifying different strains of the nematode worm C. elegans based on their time series of their movement speed. The repository, with links to pre-computed HCTSA.mat files, is here.
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