We propose a method to detect trending topics on twitter before they actually become trending by means of a non-parametric approach to classify time-series data. The approach builds on the following simple hypothesis -- tweets are generated by humans, humans fundamentally exhibit simple behavior and hence there are only `few' ways in which a topic can become trending. We shall discuss high accuracy of our method using empirical study as well as theoretical guarantees under a meaningful statistical model.