# Which model should I prefer for time series forecasting?

I have time series as

0.4385487 0.7024281 0.9381081 0.8235792 0.7779642 1.1670665 1.1958634 1.1958634 0.8235792 0.8530141 0.8802216 1.1958634 1.1235897 1.3542734 1.3245534 0.9381081 1.1670665 1.1958634 0.8802216 1.3542734 1.1670665 4.9167998 0.9651803 0.8221709 1.1070461 1.2006974 1.3542734 0.9651803 0.9381081 0.9651803 0.8854192 1.3245534 1.1235897 1.2006974 1.1958634 0.4385487 1.3245534 4.9167998 1.2277843 0.8530141 1.0018480 0.3588158 0.8530141 0.8867365 1.3542734 1.1958634 1.1958634 0.9651803 0.8802216 0.8235792 4.9167998 1.1958634 0.9651803 0.8854192 0.8854192 1.2006974 0.8867365 0.9381081 0.8235792 0.9651803 0.4385487 0.9936722 0.8821301 1.3542734 1.1235897 1.6132899 1.3245534 1.3542734 0.8132233 0.8530141 1.1958634 1.2279813 0.8354292 1.3578511 1.1070461 0.8530141 0.9670581 1.1958634 0.7779642 1.2006974 1.1958634 0.8235792 1.3245534 0.5119648 2.3386331 0.8890464 0.8867365 4.9167998 1.2006974 1.2006974 0.6715839 4.9167998 0.7747481 4.9167998 0.8867365 1.2277843 0.8890464 1.2277843 0.8890464 1.0541099 0.8821301


I am using package "itsmr"-autofit(),"forecast"-auto.arima(),"package"--functions

1. Autoregressive model

> ar(t)

Call:
ar(x = t)

Order selected 0  sigma^2 estimated as  0.9222

2. ARMA model

> autofit(t)
$phi [1] 0$theta
[1] 0

$sigma2 [1] 0.9130698$aicc
[1] 279.4807

$se.phi [1] 0$se.theta
[1] 0

3. ARIMA model

    > auto.arima(t)
Series: t
ARIMA(0,0,0) with non-zero mean

Coefficients:
intercept
1.2623
s.e.     0.0951

sigma^2 estimated as 0.9131:  log likelihood=-138.72
AIC=281.44   AICc=281.56   BIC=286.67


The auto.arima function automatically differences time series: we don't have to worry about transformation.

> auto.arima(AirPassengers)
Series: AirPassengers
ARIMA(0,1,1)(0,1,0)[12]

Coefficients:
ma1
-0.3184
s.e.   0.0877

sigma^2 estimated as 137.3:  log likelihood=-508.32
AIC=1020.64   AICc=1020.73   BIC=1026.39


Which model should I select to get p,q values & for forecasting purpose?

• It is striking that the majority of the values in this time series occur multiple times (one as many as 11 times). How were these values measured? – whuber May 18 '12 at 20:28

• i don't know what is "Autobox",i search on net & take first hit http://www.autobox.com/cms/` but it gets error like "Database Error: Unable to connect to the database:Could not connect to MySQL" i want p(autoregressive order) & q(moving avg. order) value of above models for further use(for making distance matrix)in my project is it like that,if above model not fit,then it is not time series data, & how can i evaluates that given sequence of numbers is time series or not ? – Sagar Nikam May 19 '12 at 6:31