Description Usage Arguments Details Value Author(s) See Also Examples
The function automatically evaluates and returns the fittest linear model
among ARIMA and polynomial regression, with and without Kalman filtering,
for prediction of a given univariate time series. Wrapper for the
fittestArima
, fittestArimaKF
,
fittestPolyR
and fittestPolyRKF
functions for
automatic time series prediction, whose results are also returned.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | fittestLM(
timeseries,
timeseries.test = NULL,
h = NULL,
level = 0.95,
na.action = stats::na.omit,
filtered = TRUE,
order = NULL,
minorder = 0,
maxorder = 5,
raw = FALSE,
initQ = NULL,
rank.by = c("MSE", "NMSE", "MAPE", "sMAPE", "MaxError", "AIC", "AICc", "BIC",
"logLik", "errors", "fitness"),
...
)
|
timeseries |
A vector or univariate time series which contains the values used for fitting the models. |
timeseries.test |
A vector or univariate time series containing a
continuation for |
h |
Number of consecutive values of the time series to be predicted. If
|
level |
Confidence level for prediction intervals. |
na.action |
A function for treating missing values in |
filtered |
See |
order |
See |
minorder |
See |
maxorder |
See |
raw |
See |
initQ |
See |
rank.by |
Character string. Criteria used for ranking candidate models. See 'Details'. |
... |
See |
The results of the best evaluated models returned by
fittestArima
, fittestArimaKF
,
fittestPolyR
and fittestPolyRKF
are ranked and
the fittest linear model for prediction of the given univariate time series
is selected based on the criteria in rank.by
.
The ranking criteria in rank.by
may be set as a prediction error
measure (such as MSE
, NMSE
, MAPE
,
sMAPE
or MAXError
), or as a fitness criteria
(such as AIC
, AICc
, BIC
or
logLik
). See fittestArima
,
fittestArimaKF
, fittestPolyR
or
fittestPolyRKF
.
If rank.by
is set as "errors"
or "fitness"
, the
candidate models are ranked by all the mentioned prediction error measures
or fitness criteria, respectively. The wheight of the ranking criteria is
equally distributed. In this case, a rank.position.sum
criterion is
produced for ranking the candidate models. The rank.position.sum
criterion is calculated as the sum of the rank positions of a model (1 = 1st
position = better ranked model, 2 = 2nd position, etc.) on each calculated
ranking criteria.
A list with components:
model |
An object containing the
fittest evaluated linear model. The class of the model object is dependent
on the results of the evaluation (ranking). See |
rank |
Data.frame with the fitness
and/or prediction accuracy criteria computed for all models considered,
ranked by |
ranked.results |
A list of lists containing
the ranked results of the functions |
Rebecca Pontes Salles
fittestArima
, fittestArimaKF
,
fittestPolyR
, fittestPolyRKF
1 2 3 4 5 6 7 8 |
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