Predicting user activities and interests has many applications, for example, in contextual recommendations. Although the problem of predicting interests in general has been studied extensively, the problem of predicting when the users are likely to act on those interests has received considerably less attention. Such predictions of timing are extremely important when the application itself is time-sensitive (e.g., travel recommendations are irrelevant too far in advance and after reservations have already been made). Particularly important is the ability to predict likely future activities long in advance (as opposed to short-term prediction of imminent activities). In this paper we describe a comprehensive study that addresses this problem of making long-term time-dependent predictions of user interest. We have conducted this study on a large collection of visits to various venues of interest performed by users of Foursquare. We have built models that, given a user's history, can predict whether or not the user will visit a venue of a particular type on a given day. These models provide useful prediction accuracy of up to 75% for up to several weeks into the future. Our study explores and compares various feature sets and prediction methods. Of particular interest is the fact that venues interact with each other: to predict visits to one type of venue, it helps to use the history of visits to all venue types.