title: Non uniform, multivariable time series in R.
body: <p>A fraction of my dataset is: </p>
  
  <pre><code> &gt; df
     v1 v2 v3         v4
   1  1  0  1 03-11-2013
   2  5  2  0 09-11-2013
   3  4  2  0 12-11-2013
   4  2  6  1 14-11-2013
   5  3  0  1 21-11-2013
   6  0  0  1 24-11-2013
  </code></pre>
  
  <p>where v4 are non-uniform dates (the real data is significantly larger). The aim is to estimate the next value of "v1" (observation number 7 in this very reduced dataset) based on the pre-exisitng information. The easiest would be perhaps using only previous values of v1, but v1, v2 and v3 are potentially related. Therefore, the ideal situation would be utilising all information.</p>
  
  <p>I tried the package forecast() in R but I find difficulties to deal with non-uniform time series. I also came accross the arima() function/models, but yet again it seems will only work with uniform time series(?). Therefore, the question would be:</p>
  
  <p>How can I predict the value of a certain variable based on non-uniform, historical information of multiple variables (including the one I want to predict) in R?    </p>