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I have three time series of the following form:

$T = T_{2000}, T_{2004}, T_{2008}, …$

$U = U_{1998}, U_{1999}, U_{2000}, U_{2001}, U_{2002}, U_{2003}, …$

$V = V_{1998}, V_{1999}, V_{2000}, V_{2001}, V_{2002}, V_{2003}, …$

I would like to predict $T$ from $U + V$, but since the time series have unequal numbers of observations ($T$ has four times less data points: the series 'coincide' only every four years), I am unsure of how to go with it.

Any help (and software recommendations, preferably with R) would be very much appreciated!

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If you want to use U & V as features what you can do is have rows equal to the number of T observations and since T has a frequency of 4 years hence have 8 features equal to the last 4 years (compared to the year you are predicting) observation of U & V each.

As an example to predict T(2004) use U(2000-2003) & V(2000-2003) as features.

This is just one such way. It might also be that your prediction of U(2008) is dependant on V(1998 ~ 2007). In such case, you might want to model using the past value of T as well since it might capture well the previous values of U & V ( for years >4 )

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  • $\begingroup$ So I should just use lags of (T, U, V)? What kind of model is that? AR(x)? $\endgroup$ – fullwheel Mar 26 at 8:53
  • $\begingroup$ You will have to check which model works, but Arima etc should work nicely. But I was talking about creating a dataset/features. It makes sense that to predict T(2004) you can only use U < 2004 & V < 2004. $\endgroup$ – Axelius Mar 26 at 8:57
  • $\begingroup$ Alright. I'll wait a bit for additional suggestions, but will start looking into ARIMA straight away. $\endgroup$ – fullwheel Mar 26 at 9:14

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