I have music play data organized by the day on which each track was played, from March 1st, 2015 to August 30th, 2015. The data set contains count data for every day a song was played. I'd like to predict play counts for each track for an out-of-time window, from 2015-09-01 to 2015-09-30.Details of the dataThe data is from Alibaba's TianChi CompetitionAction Table (mars_tianchi_user_actions)Any users action data for a song is indicated by a unique rowSongs Tablemars_tianchi_songsResult SetThe participants need to predict the artists plays data in the following two months (20150901-20151030).Participants Result Table (mars_tianchi_artist_plays_predict)For song id daa234f183aee2373d20987b247cd768(all the song ids are hash values). The play plot is:As you can see it, the variance of each day's play for this track is quite large.I also have data on who played each track, with each user ids represented as a hash value.I'd like some guidance on which analysis method I could use here. I am considering time series analysis.
How should I process music play data
machine learning;data mining;predictive modeling;time series