Univariate time series forecasts for short and long time series data using the appropriate model.
ts.forecast(ts_modelx, h=1, tojson=F)
| ts_modelx | The input univariate time series data |
|---|---|
| h | The number of prediction steps |
| tojson | If TRUE the results are returned in json format, default returns a list |
A list with the parameters:
ts.model a string indicating the arima orders
data_year The time that time series data were sampled
data The time series values
predict_time The time that defined by the prediction_steps parameter
predict_values The predicted values that defined by the prediction_steps parameter
up80: The upper limit of the 80% predicted confidence interval
low80: The lower limit of the 80% predicted confidence interval
up95: The upper limit of the 95% predicted confidence interval
low95: The lower limit of the 95% predicted confidence interval
This function is used internally in ts.analysis and forecasts the model that fits the input data using the auto.arima function(see forecast package). The model selection depends on the results of some diagnostic tests (acf,pacf,pp adf and kpss). For short time series the selected arima model is among various orders of the AR part using the first differences and the first order moving average component, with the lower AIC value.
Athens_draft <- ts.non.seas.model(Athens_draft_ts) #Hold the model object of non seasonal modeling draft<-Athens_draft$model.summary ts.forecast(draft)#> $ts.model #> [1] "ARIMA(1,1,1)" #> #> $data_year #> Time Series: #> Start = 2004 #> End = 2015 #> Frequency = 1 #> [1] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 #> #> $data #> Time Series: #> Start = 2004 #> End = 2015 #> Frequency = 1 #> [1] 720895000 628937000 618550000 724830000 858942000 919508000 977488000 #> [8] 931607000 866517393 667108000 773422555 759559284 #> #> $predict_time #> Time Series: #> Start = 2016 #> End = 2016 #> Frequency = 1 #> [1] 2016 #> #> $predict_values #> Time Series: #> Start = 2016 #> End = 2016 #> Frequency = 1 #> [1] 757082035 #> #> $up80 #> Time Series: #> Start = 2016 #> End = 2016 #> Frequency = 1 #> 80% #> 891925346 #> #> $low80 #> Time Series: #> Start = 2016 #> End = 2016 #> Frequency = 1 #> 80% #> 622238724 #> #> $up95 #> Time Series: #> Start = 2016 #> End = 2016 #> Frequency = 1 #> 95% #> 963307083 #> #> $low95 #> Time Series: #> Start = 2016 #> End = 2016 #> Frequency = 1 #> 95% #> 550856987 #>