title: R ARIMA model giving odd results
body: <p>I'm trying to use an ARIMA model in R to forecast data. A slice of my time series looks like this:</p>
  
  <p><img src="https://i.stack.imgur.com/ar6zS.png" alt="enter image description here"></p>
  
  <p>This is just a slice of time for you get a sense of it. I have daily data from 2010 to 2015.</p>
  
  <p>I want to forecast this into the future. I'm using the <code>forecast</code> library, and my code looks like this:</p>
  
  <pre><code>dt = msts(data$val, seasonal.periods=c(7, 30))
  fit = auto.arima(dt)
  plot(forecast(fit, 300))
  </code></pre>
  
  <p>This results in:</p>
  
  <p><img src="https://i.stack.imgur.com/SKZLf.png" alt="enter image description here"></p>
  
  <p>This model isn't good or interesting. My <code>seasonal.periods</code> were defined by me because I expect to see weekly and monthly seasonality, but the result looks the same with no seasonal periods defined.</p>
  
  <p>Am I missing something? Very quickly the forecast predictions change very, very little from point to point.</p>
  
  <p>Edit: </p>
  
  <p>To further show what I'm talking about, here's a concrete example. Let's say I have the following fake dataset:</p>
  
  <pre><code>x = 1:500
  y = 0.5*c(NA, head(x, -1)) - 0.4*c(NA, NA, head(x, -2)) + rnorm(500, 0, 5)
  </code></pre>
  
  <p>This is an AR(2) model with coefficients <code>0.5</code> and <code>0.4</code>. Plotting this time series yields:</p>
  
  <p><img src="https://i.stack.imgur.com/BcDe0.png" alt="enter image description here"></p>
  
  <p>So I create an ARIMA model of this and plot the forecast results:</p>
  
  <pre><code>plot(forecast(auto.arima(y), 300))
  </code></pre>
  
  <p>And the results are:</p>
  
  <p><img src="https://i.stack.imgur.com/66IaT.png" alt="enter image description here"></p>
  
  <p>Why can't the ARIMA function learn this obvious model? I don't get any better results if I use the <code>arima</code> function and force it to try an AR(2) model.</p>