# TSAnalysis: time series analysis and state-space modelling

**URL:** https://discourse.julialang.org/t/tsanalysis-time-series-analysis-and-state-space-modelling/30672
**Category:** Package Announcements
**Tags:** statistics, time-series, machine-learning
**Created:** [November 4, 2019, 1:04am UTC](https://discourse.julialang.org/t/tsanalysis-time-series-analysis-and-state-space-modelling/30672 "2019-11-04T01:04:45Z")
**Posts on this page:** 1
**Showing post:** 11

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### Author: ![fipelle](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/fipelle/32/4772_2.png) [@fipelle](https://discourse.julialang.org/u/fipelle)
#### Post date: [November 29, 2019, 12:23am UTC](https://discourse.julialang.org/t/tsanalysis-time-series-analysis-and-state-space-modelling/30672/11 "2019-11-29T00:23:11Z")

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Hi all,

I have released a new version (v.0.1.2) and it is now being registered. I do not think that this is such a major release to require a new post. However, there are a few important changes:

- I have added support for ARMA and ARIMA models;

- I have added tests for the Kalman filter, smoother, ARMA and ARIMA models - this might be less appreciable from a user perspective, but it makes the package more robust;

- I solved minor bugs and deprecated a few methods that were not being used by any function;

- I updated the README.md and included two small examples in the /examples/ folder.

* * *

Considering the current status of the project I think that it would be easier to proceed as follows:

- Build on the current functions for ARIMA / ARMA and add support for VARIMA / VARMA models (this should be in v0.1.3);

- Add support for textbook univariate state-space models (v0.1.4);

- Add a range of time series functions (e.g., ACF, CCF, …) and time varying versions of the most used models (v.0.1.5 and v0.1.6);

- Less certain: extend TVP to all models and release v0.2.0.

Note: in the examples, I substituted `SimulatedAnnealing()` with `NelderMead()`. Empirically, `NelderMead()` seems worse on my data. However, it is less computationally expensive and this might help estimating ARIMAs and more complicated models on standard laptops.

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