title: R time-series forecasting with auto.arima and xreg=explanatory variables
body: <p>I have lots of time-series (retail data) and I want to make forecast for all of them.</p>
  
  <p>For example  let's take a look at one of them:</p>
  
  <pre><code>   &gt; dput(x)
   c(1774, 1706, 1288, 1276, 2350, 1821, 1712, 1654, 1680, 1451, 
   1275, 2140, 1747, 1749, 1770, 1797, 1485, 1299, 2330, 1822, 1627, 
   1847, 1797, 1452, 1328, 2363, 1998, 1864, 2088, 2084, 594, 884, 
   1968, 1858, 1640, 1823, 1938, 1490, 1312, 2312, 1937, 1617, 1643, 
   1468, 1381, 1276, 2228, 1756, 1465, 1716, 1601, 1340, 1192, 2231, 
   1768, 1623, 1444, 1575, 1375, 1267, 2475, 1630, 1505, 1810, 1601, 
   1123, 1324, 2245, 1844, 1613, 1710, 1546, 1290, 1366, 2427, 1783, 
   1588, 1505, 1398, 1226, 1321, 2299, 1047, 1735, 1633, 1508, 1323, 
   1317, 2323, 1826, 1615, 1750, 1572, 1273, 1365, 2373, 2074, 1809, 
   1889, 1521, 1314, 1512, 2462, 1836, 1750, 1808, 1585, 1387, 1428, 
   2176, 1732, 1752, 1665, 1425, 1028, 1194, 2159, 1840, 1684, 1711, 
   1653, 1360, 1422, 2328, 1798, 1723, 1827, 1499, 1289, 1476, 2219, 
   1824, 1606, 1627, 1459, 1324, 1354, 2150, 1728, 1743, 1697, 1511, 
   1285, 1426, 2076, 1792, 1519, 1478, 1191, 1122, 1241, 2105, 1818, 
   1599, 1663, 1319, 1219, 1452, 2091, 1771, 1710, 2000, 1518, 1479, 
   1586, 1848, 2113, 1648, 1542, 1220, 1299, 1452, 2290, 1944, 1701, 
   1709, 1462, 1312, 1365, 2326, 1971, 1709, 1700, 1687, 1493, 1523, 
   2382, 1938, 1658, 1713, 1525, 1413, 1363, 2349, 1923, 1726, 1862, 
   1686, 1534, 1280, 2233, 1733, 1520, 1537, 1569, 1367, 1129, 2024, 
   1645, 1510, 1469, 1533, 1281, 1212, 2099, 1769, 1684, 1842, 1654, 
   1369, 1353, 2415, 1948, 1841, 1928, 1790, 1547, 1465, 2260, 1895, 
   1700, 1838, 1614, 1528, 1268, 2192, 1705, 1494, 1697, 1588, 1324, 
   1193, 2049, 1672, 1801, 1487, 1319, 1289, 1302, 2316, 1945, 1771, 
   2027, 2053, 1639, 1372, 2198, 1692, 1546, 1809, 1787, 1360, 1182, 
   2157, 1690, 1494, 1731, 1633, 1299, 1291, 2164, 1667, 1535, 1822, 
   1813, 1510, 1396, 2308, 2110, 2128, 2316, 2249, 1789, 1886, 2463, 
   2257, 2212, 2608, 2284, 2034, 1996, 2686, 2459, 2340, 2383, 2507, 
   2304, 2740, 1869, 654, 1068, 1720, 1904, 1666, 1877, 2100, 504, 
   1482, 1686, 1707, 1306, 1417, 2135, 1787, 1675, 1934, 1931, 1456)
  </code></pre>
  
  <p>I want to make a forecast with auto.arima model:</p>
  
  <pre><code>y=auto.arima(x)
  plot(forecast(y,h=30))
  points(1:length(x),fitted(y),type="l",col="green")
  </code></pre>
  
  <p><img src="https://i.stack.imgur.com/uwSqY.png" alt="enter image description here"></p>
  
  <p>There are abnormally high sales near indices 280-300. I know, that there were some fests.
  I want to feed those to my forecasting model as explanatory variables.</p>
  
  <p>I have a vector holiday, where TRUE --- explanatory variables.</p>
  
  <pre><code>&gt; dput(holiday)
  c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  FALSE, FALSE, FALSE)
  </code></pre>
  
  <p>I try to add that explanatory dates to the model:</p>
  
  <pre><code>&gt; auto.arima(x, stepwise=FALSE,approx=FALSE, xreg=holiday)
  Series: x 
  ARIMA(2,1,3)                    
  
  Coefficients:
            ar1      ar2      ma1     ma2      ma3    holiday
        -0.4682  -0.9568  -0.2008  0.4029  -0.8424  -354.5092
  s.e.   0.0173   0.0169   0.0398  0.0338   0.0412   112.5610
  
  sigma^2 estimated as 85849:  log likelihood=-2245.74
  AIC=4505.49   AICc=4505.85   BIC=4531.8
  </code></pre>
  
  <p>Seems like it is working, but when I try to plot it fails with error:</p>
  
  <pre><code>y&lt;-auto.arima(x, stepwise=FALSE,approx=FALSE, xreg=holiday)
  &gt; plot(forecast(y,h=30))
  Error in plot(forecast(y, h = 30)) : 
    error in evaluating the argument 'x' in selecting a method for function 'plot': Error     in forecast.Arima(y, h = 30) : No regressors provided
  </code></pre>
  
  <p>Maybe I'm doing something wrong? how can I add explanatory variables to the model and then plot forecast?</p>