title: How to use machine learning algorithms to find special points in a stream of values?
body: <p>I am monitoring individuals every second for a single statistic whose value is between 0 and 1. These individuals may occasionally perform some intense activity which is reflected in the behaviour of this statistic (by oscillating rapidly within a short period of time, for example).</p>
  
  <p>I would like to use the statistic to detect the presence of this activity.</p>
  
  <p>My training set thus consists of long streams of values, with certain highlighted intervals where the intense activity is carried out. For example one of my streams may look like this:</p>
  
  <p>... 0.1 0.2 0.2 0.3 0.4 0.4 0.4 <b><em>0.7 0.1 0.6 0.2 0.9</em></b> 0.8 0.7 0.7 0.7 0.6 ...</p>
  
  <p>(the period of the intense activity is emphasized in <b><i>bold italics</i></b>)</p>
  
  <p>With some basic knowledge of elementary machine learning algorithms, I can think of several ways to try to solve this problem. One of them would be to have my training sets be equally sized strings of values and use logistic regression to detect whether they contain or not a period of intense activity. However this has the disadvantage of me having to decide this length in advance, and having training values which may contain only a section of a period of intense activity.</p>
  
  <p>Is there a standard way to approach this as a machine learning problem?</p>