Model fit of non seasonal time series
ts.non.seas.model(tsdata, x.ord=NULL, tojson=F)
| tsdata | The input univariate non seasonal time series data |
|---|---|
| x.ord | An integer vector of length 3 specifying the order of the Arima model |
| tojson | If TRUE the results are returned in json format, default returns a list |
A list with the following components:
model.summary:
ts_model The summary model details returned as Arima object for internal use in ts.analysis function
model:
ts_model:
arima.order The Arima order
arima.coef A vector of AR, MA and regression coefficients
arima.coef.se The standard error of the coefficients
residuals: The residuals of the model (fitted innovations)
compare:
variance.coef The matrix of the estimated variance of the coefficients
resid.variance The MLE of the innovations variance
not.used.obs The number of not used observations for the fitting
used.obs the number of used observations for the fitting
loglik The maximized log-likelihood (of the differenced data), or the approximation to it used
aic The AIC value corresponding to the log-likelihood
bic The BIC value corresponding to the log-likelihood
aicc The second-order Akaike Information Criterion corresponding to the log-likelihood
Model fit of non seasonal time series using arima models of non seasonal time series data. The model with the lowest AIC value is selected for forecasts.
ts.non.seas.model(Athens_draft_ts)#> $model.summary #> Series: tsdata #> ARIMA(1,1,1) #> #> Coefficients: #> ar1 ma1 #> 0.1441 0.0153 #> s.e. 0.8665 0.8392 #> #> sigma^2 estimated as 1.107e+16: log likelihood=-217.7 #> AIC=441.41 AICc=444.84 BIC=442.6 #> #> $model #> $model$arima.order #> [1] 1 1 0 0 1 1 0 #> #> $model$arima.coef #> ar1 ma1 #> 0.14413158 0.01530798 #> #> $model$arima.coef.se #> ar1 ma1 #> 0.8665 0.8392 #> #> #> $residuals_fitted #> $residuals_fitted$residuals #> Time Series: #> Start = 2004 #> End = 2015 #> Frequency = 1 #> [1] 720894.6 -90787238.6 4239112.9 107712202.6 117144839.4 #> [6] 39442974.7 48646734.4 -54982432.3 -57635036.0 -189145649.1 #> [11] 137951183.7 -31298309.8 #> #> $residuals_fitted$fitted #> Time Series: #> Start = 2004 #> End = 2015 #> Frequency = 1 #> [1] 720174105 719724239 614310887 617117797 741797161 880065025 928841266 #> [8] 986589432 924152429 856253649 635471371 790857594 #> #> $residuals_fitted$time #> Time Series: #> Start = 2004 #> End = 2015 #> Frequency = 1 #> [1] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 #> #> $residuals_fitted$line #> [1] 0 #> #> #> $compare #> $compare$resid.variance #> [1] 1.107099e+16 #> #> $compare$variance.coef #> ar1 ma1 #> ar1 0.7507992 -0.6832262 #> ma1 -0.6832262 0.7042599 #> #> $compare$not.used.obs #> [1] 0 #> #> $compare$used.obs #> [1] 11 #> #> $compare$loglik #> [1] -217.7045 #> #> $compare$aic #> [1] 441.409 #> #> $compare$bic #> [1] 442.6027 #> #> $compare$aicc #> [1] 444.8376 #> #>