Hi,

Thank you for your feedback!

@Datseris You are correct, I am not. For now, I am supporting linear state-space models à la Harvey (1990), Durbin and Koopman (2012), and co-authors. There is a small example with `kforecast`

in the readme, but I recon that it is not great. I will expand on that

@nilshg Yes. In the coming releases I will try to extend support to the following methods:

- ARIMA models
- Standard (aka textbook) univariate decompositions (e.g., seasonal adjustments)
- ACFs ,CCFs and other basic functions for time series analysis. I know there is plenty of support already, but I feel that they should be included in a TS package.

After, it would be nice to cover TVP versions of the models above. I am not planning to release estimation algorithms anytime soon - I think I will rely on `Optim`

for now.

Bibliography

- Harvey, A. C. (1990).
*Forecasting, structural time series models and the Kalman filter*. Cambridge university press. - Durbin, J., & Koopman, S. J. (2012).
*Time series analysis by state space methods*. Oxford university press.