Discover @felipeelorrieta Open Source projects

Felipe Elorrieta (@felipeelorrieta)

felipeelorrieta

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Location 🇨🇱 Chile
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IAR_Model

This folder contains R and Python functions to fit unequally spaced time series from the Irregular Autoregressive (IAR). From the functions of this folder we can generate observations for each process, compute the negative of the log likelihood of these process, fit each model to irregularly sampled data, and test the significance of the estimated parameters.

iAR

iAR-package

Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model.

CIAR_Model

This folder contains Python functions to fit unequally spaced time series from the Complex Irregular Autoregressive Model (CIAR model). From the functions of this folder we can generate observations for each process, compute the negative of the log likelihood of these process, fit each model to irregularly sampled data, and test the significance of the estimated parameters.

Jupyter Notebook

AITSPackage-R

This package contains a set of R functions and datasets to perform both the Irregular Autoregressive Model (IAR model) and the Complex Irregular Autoregressive Model (CIAR model) which are useful for unequally spaced time series.