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## Description:
I am planning to implement various causal discovery methods for time series data. The methods I am particularly interested in include CDAN, ACD, TiMINo, and NTS-NOTEARS. Each of these…
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Introduce some non-linear time series models. Two-regime threshold AR (TAR) models are good candidates.
Original Paper :
Howell Tong
Department of Statistics, The Chinese University of Hong K…
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Section 5.2 of the Lancaster review:
> The discrete wavelet transform has also been used as the basis of multifractal surrogates, which aim to enable testing of nonlinear interdependence within, wi…
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According to [1] iaaft and aaft are not working for irregular time series, because these methods are based on the Fourier transform.
They propose an alternative to it using the LombScargle periodogr…
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Thank you for your great work which offers new approach for symbolic regression. By your example in the slice, FFX can output the function of linear or nonlinear expression. But when I study the stoc…
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# Goal
As a developer, I want to implement nonlinear regression models, so that I can develop statistical indicators.
# Consider
- Consider using facebook's [prophet](https://github.com/facebook/pro…
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there are several issues related to this
#3224 transformation to normality
#2791 backtransform for transformed endog, e.g. log(endog) in time series, smearing
also related delta method
This iss…
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1. How to select an ideal forecasting method? (~ Ward et al. 2014)
* evaluate forecasting approaches across different time series
* do time series properties influence which methods (or para…
ha0ye updated
5 years ago
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### Bug description
Surface Level: When the parameters are a nested series, (using numerical differensiation and "scipy_slsqp"). The function calls works as intended. But after a few calls, the cod…
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It would be nice to have a Julia-native implementation of the (relatively) new PCMCI [1] algorithm for causal inference in time series proposed by @jakobrunge and his team.
The algorithm essentiall…