title: Create a ts R object from a CSV file with dates as a column with multivariate series
body: <p>I have a CSV file with the format</p>
  
  <pre><code>ref_date;wings;airfoil;turbines
  2015-03-31;123,22;22,77;99,0
  2015-04-30;123,22;28,77;99,0
  2015-05-31;123,22;22,177;02,0
  2015-06-30;56,288;22,77;99,0
  </code></pre>
  
  <p>and I want to use the <code>forecast</code> package to predict the next values of this time series. The <code>forecast</code> package only accepts a <code>ts</code> object, but so far all my attempts to create one failed. I tried to</p>
  
  <p>1) Use zoo package</p>
  
  <pre><code>df = read.zoo(data_file, sep=';', dec=',', format="%Y-%m-%d", header=T)
  </code></pre>
  
  <p>but the data is truncated at the decimal point. </p>
  
  <p>2) Use the zoo package with xts</p>
  
  <pre><code>df = read.zoo(datafile, sep=';', dec=',', format="%Y-%m-%d", header=T)
  df_ts = ts(df)
  </code></pre>
  
  <p>The dates are nowhere to be seen, the index is just a sequence of numbers, like</p>
  
  <pre><code>1 123.22 22.77 99
  </code></pre>
  
  <p>3) Use read.csv and ts</p>
  
  <pre><code>df = read.zoo(datafile, sep=';', dec=',', format="%Y-%m-%d", header=T)
  df_ts = ts(df)
  </code></pre>
  
  <p>4) Try using xts</p>
  
  <pre><code>df = read.csv(data_file, sep=';', header=T, dec=',')
  tt = as.xts(df[,-1],order.by = as.Date(as.character(df[,1]), format = "%Y-%m-%d"))
  forecast(tt) 
  Error in `tsp&lt;-`(`*tmp*`, value = tsp.y) : 
    invalid time series parameters specified
  </code></pre>
  
  <p>the result looses all information about the date, including the ref_date column, and now the forecast package gives nonsense as result.</p>
  
  <p>What is the correct approach to create the object that the <code>forecast</code> library is waiting and can generate a forecast, maintaining the dates, including in the plots?</p>