uotm - Uncertainty of Time Series Model Selection Methods
We propose a new procedure, called model uncertainty
variance, which can quantify the uncertainty of model selection
on Autoregressive Moving Average models. The model uncertainty
variance not pay attention to the accuracy of prediction, but
focus on model selection uncertainty and providing more
information of the model selection results. And to estimate the
model measures, we propose an simplify and faster algorithm
based on bootstrap method, which is proven to be effective and
feasible by Monte-Carlo simulation. At the same time, we also
made some optimizations and adjustments to the Model Confidence
Bounds algorithm, so that it can be applied to the time series
model selection method. The consistency of the algorithm result
is also verified by Monte-Carlo simulation. We propose a new
procedure, called model uncertainty variance, which can
quantify the uncertainty of model selection on Autoregressive
Moving Average models. The model uncertainty variance focuses
on model selection uncertainty and providing more information
of the model selection results. To estimate the model
uncertainty variance, we propose an simplified and faster
algorithm based on bootstrap method, which is proven to be
effective and feasible by Monte-Carlo simulation. At the same
time, we also made some optimizations and adjustments to the
Model Confidence Bounds algorithm, so that it can be applied to
the time series model selection method. The consistency of the
algorithm result is also verified by Monte-Carlo simulation.
Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019)
Model Confidence Bounds for Variable Selection. Biometrics,
75:392-403.<DOI:10.1111/biom.13024> for more information.
