Using Lasso Procedure for Variables Selection of Autoregressive Model for High Dimensional Time Series of Caenorhabditis Elegans Motion
DOI:
https://doi.org/10.62933/jxdyj297Keywords:
High dimensional time series, , Auto-regressive,, Variable selection, , LASSO, , Caenorhabditis elegans motionAbstract
The Lasso is a common model for selection and also is a common estimation procedure for linear models. In this study, Lasso estimator will be used for obtaining more fitted autoregressive time series models. Simulation procedure has been used to generate a time series of the motion caenorhabditis elegans (CE represented by the tan-angles of wave-motion). Each observation of this time series is a recorded frame (0.5 second) of 2.5 hours video of CE motion. in this study, the real and simulated univariate time series of CE motion (tan-angles) are modeled via Lasso and autoregressive models (hybrid Lasso-AR approch) after multi-processes of variable selection. The results of simulated and real univariate time series reflects more fitted models after performing variables selection procedure. In conclusion, hybrid Lasso-AR approach can be used for best high dimensional time series modelling.
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