Highly comparative time-series analysis with hctsa
  • Information about hctsa
    • Introduction
    • Getting started
    • Publications using hctsa
    • UMAP Projections
    • Related Time-Series Resources
    • List of included code files
    • FAQ
  • Installing and using hctsa
    • General advice and common pitfalls
    • Installing and setting up
      • Structure of the hctsa framework
      • Overview of an hctsa analysis
      • Compiling binaries
    • Running hctsa computations
      • Input files
      • Performing calculations
      • Inspecting errors
      • Working with hctsa files
    • Analyzing and visualizing results
      • Assigning group labels to data
      • Filtering and normalizing
      • Clustering rows and columns
      • Visualizing the data matrix
      • Plotting the time series
      • Low dimensional representation
      • Finding nearest neighbors
      • Investigating specific operations
      • Exploring classification accuracy
      • Finding informative features
      • Interpreting features
      • Comparing to existing features
      • Working with short time series
    • Working with a mySQL database
      • Setting up the mySQL database
      • The database structure
      • Populating the database with time series and operations
      • Adding time series
      • Retrieving from the database
      • Computing operations and writing back to the database
      • Cycling through computations using runscripts
      • Clearing or removing data
      • Retrieving data from the database
      • Error handling and maintenance
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  1. Information about hctsa

FAQ

Frequently asked questions pertaining to the use of hctsa.

Select a drop-down for more information:

How can I use more machine-learning algorithms rather than SVM linear only using TS_Classify?

You can specify the classification algorithm in the cfnParams structure. You can see the options in GiveMeCfn. Typically because there is complexity in the embedding in a high-dimensional feature space, we have tried to remove complexity in the classifiers (to avoid overfitting), also for interpretability. You can also use OutputToCSV and use the hctsa data in other environments (like python), [if this route, feel free to share your python workflow here]

Can I perform a multivariate time series analysis using hctsa?

hctsa is designed to extracting thousands of features from a single univariate time series; pyspi implements hundreds of pairwise dependence measures from multivariate time series.

You could incorporate both (i) univariate features of the components of the system; and (ii) features summarizing the pairwise dependence structure of the distributed system.

For example, some points to consider:

  • You could compute univariate features of each component of your system and then concatenate them to combine features of all individual time series:

    • To do this you may consider using a reduced set of features, like catch22 to avoid massive dimensionality explosion.

    • You may alternatively use hctsa and then do feature selection or dimensionality reduction to tailor a reduced feature set to your application.

    • You may also consider adding some pairwise dependence measures to summarize the multivariate structure, cf. pyspi.

  • You could compute univariate features of extracted dominant components of your multivariate system, e.g., using PCA.

How can I export the extracted features?

Use OutputToCSV. This gives you .csv files corresponding to a given hctsa calculation that you can analyze however you please.

Are there any plans to move away from Matlab (which is proprietary and thus not accessible to users perhaps outside universities) to perhaps Python/R/Julia?

Most users are within Universities with a Matlab license, so I haven't come across issues with this (and the main analysis code is licensed non-commercial anyway). But there are a couple solutions if this comes up:

  • Use an alternative (e.g., native R or native python) feature-extraction tool, such as those listed here. These have far fewer features than hctsa but can get you some of the way there.

  • Use Matlab temporarily to derive a reduced set of useful features for your problem, and then implement them (or find non-Matlab implementations). This pipeline is demonstrated (and implemented) in catch22 and we currently have new reduced ones in development. Our goal is to code reduced feature sets in C so they can be efficiently used in any programming language.

  • Also note that although it doesn't get around the license issue, you can run hctsa from python using pyopy.

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Last updated 7 months ago

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