TSPred (version 5.1)

fittestArimaKF: Automatic ARIMA fitting and prediction with Kalman filter

Description

The function predicts and returns the next n consecutive values of a univariate time series using the best evaluated ARIMA model automatically fitted with Kalman filter. It also evaluates the fitness of the produced model, using AICc, AIC, BIC and logLik criteria, and its prediction accuracy, using the MSE, NMSE, MAPE, sMAPE and maximal error accuracy measures.

Usage

fittestArimaKF(
  timeseries,
  timeseries.test = NULL,
  h = NULL,
  na.action = stats::na.omit,
  level = 0.9,
  filtered = TRUE,
  initQ = NULL,
  rank.by = c("MSE", "NMSE", "MAPE", "sMAPE", "MaxError", "AIC", "AICc", "BIC",
    "logLik", "errors", "fitness"),
  ...
)

Arguments

timeseries

A vector or univariate time series which contains the values used for fitting an ARIMA model with Kalman filter.

timeseries.test

A vector or univariate time series containing a continuation for timeseries with actual values. It is used as a testing set and base for calculation of prediction error measures. Ignored if NULL.

h

Number of consecutive values of the time series to be predicted. If h is NULL, the number of consecutive values to be predicted is assumed to be equal to the length of timeseries.test. Required when timeseries.test is NULL.

na.action

A function for treating missing values in timeseries and timeseries.test. The default function is na.omit, which omits any missing values found in timeseries or timeseries.test.

level

Confidence level for prediction intervals. See the predict.SSModel function in the KFAS package.

filtered

If filtered is TRUE, Kalman filtered time series observations are used for prediction, otherwise, Kalman smoothed observations are used for prediction.

initQ

Numeric argument regarding the initial value for the covariance of disturbances parameter to be optimized over. The initial value to be optimized is set to exp(initQ). See the Q argument of the SSMarima function in the KFAS package and the examples in KFAS. If NULL, initQ is automatically set. See 'Details'.

rank.by

Character string. Criteria used for ranking candidate models generated using different options of values for initQ. Only used if initQ is NULL. Ignored otherwise. See 'Details'.

...

Additional arguments passed to the auto.arima modelling function.

Value

A list with components:

model

An object of class "SSModel" containing the best evaluated ARIMA model fitted with Kalman Filter.

initQ

The initQ argument provided (or automatically selected) for optimization of the best evaluated ARIMA model fitted with Kalman Filter.

AICc

Numeric value of the computed AICc criterion of the best evaluated model.

AIC

Numeric value of the computed AIC criterion of the best evaluated model.

BIC

Numeric value of the computed BIC criterion of the best evaluated model.

logLik

Numeric value of the computed log-likelihood of the best evaluated model.

pred

A list with the components mean, lower and upper, containing the predictions of the best evaluated model and the lower and upper limits for prediction intervals, respectively. All components are time series. See predict.SSModel.

MSE

Numeric value of the resulting MSE error of prediction. Require timeseries.test.

NMSE

Numeric value of the resulting NMSE error of prediction. Require timeseries.test.

MAPE

Numeric value of the resulting MAPE error of prediction. Require timeseries.test.

sMAPE

Numeric value of the resulting sMAPE error of prediction. Require timeseries.test.

MaxError

Numeric value of the maximal error of prediction. Require timeseries.test.

rank.val

Data.frame with the fitness or prediction accuracy criteria computed for all candidate ARIMA with Kalman filter models ranked by rank.by. It has the attribute "ranked.models", which is a list of objects of class "SSModel" containing all the candidate ARIMA models fitted with Kalman Filter, also ranked by rank.by. Only provided if initQ was automatically selected.

rank.by

Ranking criteria used for ranking candidate models and producing rank.val.

Details

A best ARIMA model is automatically fitted by the auto.arima function in the forecast package. The coefficients of this model are then used as initial parameters for optimization of a state space model (SSModel) using the Kalman filter and functions of the KFAS package (see SSMarima and artransform).

If initQ is NULL, it is automatically set as either log(var(timeseries)) or 0. For that, a set of candidate ARIMA state space models is generated by different initial parameterization of initQ during the model optimization process. The value option which generates the best ranked candidate ARIMA model acoording to the criteria in rank.by is selected.

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). In the former case, the candidate models are used for time series prediction and the error measures are calculated by means of a cross-validation process. In the latter case, the candidate models are fitted and fitness criteria are calculated based on all observations in timeseries.

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.

References

R.J. Hyndman and G. Athanasopoulos, 2013, Forecasting: principles and practice. OTexts.

R.H. Shumway and D.S. Stoffer, 2010, Time Series Analysis and Its Applications: With R Examples. 3rd ed. 2011 edition ed. New York, Springer.

See Also

fittestArima, fittestLM, marimapred

Examples

# NOT RUN {
# }
# NOT RUN {
data(CATS,CATS.cont)
fArimaKF <- fittestArimaKF(CATS[,2],CATS.cont[,2])
#predicted values
pred <- fArimaKF$pred

#extracting Kalman filtered and smoothed time series from the best fitted model
fs <- KFAS::KFS(fArimaKF$model,filtering=c("state","mean"),smoothing=c("state","mean"))
f <- fitted(fs, filtered = TRUE) #Kalman filtered time  series
s <- fitted(fs) #Kalman smoothed time  series
#plotting the time series data
plot(c(CATS[,2],CATS.cont[,2]),type='o',lwd=2,xlim=c(960,1000),ylim=c(200,600),
 xlab="Time",ylab="ARIMAKF")
#plotting the Kalman filtered time series
lines(f,col='red',lty=2,lwd=2)
#plotting the Kalman smoothed time series
lines(s,col='green',lty=2,lwd=2)
#plotting predicted values
lines(ts(pred$mean,start=981),lwd=2,col='blue')
#plotting prediction intervals
lines(ts(pred$upper,start=981),lwd=2,col='light blue')
lines(ts(pred$lower,start=981),lwd=2,col='light blue')
# }
# NOT RUN {
# }