Package 'uotm'

Title: Uncertainty of Time Series Model Selection Methods
Description: 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.
Authors: Heming Deng Developer [aut, cre, cph], Sunan Gao Dev [aut]
Maintainer: Heming Deng Developer <[email protected]>
License: GPL-3
Version: 0.1.6
Built: 2025-02-26 03:30:06 UTC
Source: https://github.com/cran/uotm

Help Index


Estimate the model confidence bounds of time series model selection method.

Description

The MCB algorithm can find a upper bound model (UBM) and a lower bound model (LBM) which are nested and where the true model is included between the two as a specific confidence level. And the width of the MCB is the difference between the number of variables in the UBM and LBM. The model selection method with a smaller width under the same confidence level or a larger confidence level under the same width has less uncertainty.

Usage

arma.mcb(data,
max.p = 5,
max.q = 5,
method = "aic",
stepwise = TRUE,
blength = 15,
btype = "mbb",
bsamples = 100,
seed = NA)

Arguments

data

A time series sequence.

max.p

The max order of AR part.

max.q

The max order of MA part.

method

Information criterion to be used in model selection.

stepwise

If TRUE, will do stepwise selection (faster). Otherwise, it searches over all models. Non-stepwise selection can be very slow, especially for seasonal models.

blength

If btype is "mbb" then blength is the fixed block length used in generating the replicate time series. If btype is "sb" then blength is the mean of the geometric distribution used to generate the block lengths. blength should be a positive integer less than the length of data.

btype

The type of simulation required to generate the replicate time series. The possible input values are "mbb" (block resampling with fixed block lengths of blength), "sb" (block resampling with block lengths having a geometric distribution with mean blength)

bsamples

A positive integer giving the number of bootstrap replicates required.

seed

Seed.

Value

The arma.mcb method returns an object of class “dataframe”, which contains the bootstrap coverage rate and the corresponding model confidence bounds of the user-chosen model selection method in the form of lower confidence bound and upper confidence bound.

Examples

library(uotm)
ts <- arma.sim(ars = c(-0.9, -1.4, -0.7, -0.6), mas = c(0.5, -0.4), nobs = 100)
tmcb <- arma.mcb(ts, max.p = 4, max.q = 4,
stepwise = TRUE, bsamples = 50, blength = 12)
# the model confidence bounds of time series model selection method
print(tmcb)

Plot the model uncertainty curve of time series model selection method.

Description

The MUC of a given model selection method with good performance will tend to arch towards the upper left corner. Moreover, the area under the MUC, marked as AMUC, can be used as a raw measure of uncertainty of uncertainty for the model selection method under examination. The larger the AMUC, the stabler the corresponding model selection method. Overall, we can decide which method has the best performance according to the shape of the MUC and the AMUC.

Usage

arma.muc(method, ...)

Arguments

method

Model selection method like "aic", "bic" and "aicc".

...

Others.

Value

The arma.muc method returns a figure, which is the model uncertainty curve of the model selection method.

Examples

library(uotm)
ts <- arma.sim(ars = c(-0.9, -1.4, -0.7, -0.6), mas = c(0.5, -0.4), nobs = 100)

tmcb1 <- arma.mcb(ts, method = "aic", max.p = 4, max.q = 4,
bsamples = 50, stepwise = TRUE, blength = 12)
arma.muc(c("aic"), tmcb1)

# compare the uncertainty of different model selection methods using muc
tmcb2 <- arma.mcb(ts, method = "bic", max.p = 4, max.q = 4,
bsamples = 50, stepwise = TRUE, blength = 12)
arma.muc(c("aic", "bic"), tmcb1, tmcb2)

Estimate the model uncertainty variance of time series model selection method.

Description

By calculating the MUV, we can judge the uncertainty of a model selection method and compare the uncertainty of different model selection methods. The lower the degree of uncertainty in the model selection method, the smaller its MUV.

Usage

arma.muv(data,
max.p = 5,
max.q = 5,
method = "aic",
stepwise = TRUE,
blength = 15,
btype = "mbb",
bsamples = 100,
seed = NA)

Arguments

data

A time series sequence.

max.p

The max order of AR part.

max.q

The max order of MA part.

method

Information criterion to be used in model selection.

stepwise

If TRUE, will do stepwise selection (faster). Otherwise, it searches over all models. Non-stepwise selection can be very slow, especially for seasonal models.

blength

If btype is "mbb" then blength is the fixed block length used in generating the replicate time series. If btype is "sb" then blength is the mean of the geometric distribution used to generate the block lengths. blength should be a positive integer less than the length of data.

btype

The type of simulation required to generate the replicate time series. The possible input values are "mbb" (block resampling with fixed block lengths of blength), "sb" (block resampling with block lengths having a geometric distribution with mean blength)

bsamples

A positive integer giving the number of bootstrap replicates required.

seed

Seed.

Value

The arma.muv method returns an object of class “double”, which is the model uncertainty variance of the model selection method.

Examples

library(uotm)
ts <- arma.sim(ars = c(-0.9, -1.4, -0.7, -0.6), mas = c(0.5, -0.4), nobs = 100)
tmuv <- arma.muv(ts, max.p = 4, max.q = 4,
stepwise = TRUE, bsamples = 50, blength = 12)
# the model uncertainty variance of time series model selection method
print(tmuv)

Plot the time series data.

Description

This function is to plot the time series figure.

Usage

arma.plot(data)

Arguments

data

Data containing time series.

Value

The arma.plot method returns a figure.

Examples

ts <- arma.sim(ars = c(-0.9, -1.4, -0.7, -0.6), mas = c(0.5, -0.4), nobs = 100)
arma.plot(ts)

Generate Random Time Series Sequence.

Description

This function is to generate a time series sequence.

Usage

arma.sim(ars, mas, nobs, c = 0, sigma = 1, seed = NA)

Arguments

ars

AR part.

mas

MA part.

nobs

Length of time series sequence.

c

Constant.

sigma

The standrad error.

seed

Set seed.

Value

The arma.sim method returns an object of class “Time Series”.

Examples

ts <- arma.sim(ars = c(-0.9, -1.4, -0.7, -0.6), mas = c(0.5, -0.4), nobs = 100)