# FAQ

Select a drop-down for more information:

<details>

<summary><strong>How can I use more machine-learning algorithms rather than SVM linear only using <code>TS_Classify</code>?</strong></summary>

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](https://github.com/benfulcher/hctsaAnalysisPython)]

</details>

<details>

<summary><strong>Can I perform a multivariate time series analysis using </strong><em><strong>hctsa</strong></em><strong>?</strong></summary>

*hctsa* is designed to extracting thousands of features from a ***single univariate time series***; [pyspi](https://github.com/olivercliff/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](https://github.com/DynamicsAndNeuralSystems/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](https://github.com/olivercliff/pyspi).
* You could compute univariate features of extracted dominant components of your multivariate system, e.g., using PCA.

</details>

<details>

<summary><strong>How can I export the extracted features?</strong></summary>

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

</details>

<details>

<summary><strong>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?</strong></summary>

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](https://github.com/benfulcher/hctsa/wiki/Related-time-series-resources). 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](https://github.com/DynamicsAndNeuralSystems/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](https://github.com/strawlab/pyopy).

</details>

***


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://time-series-features.gitbook.io/hctsa-manual/information-about-hctsa/faq.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.