aydinhan

It is simple as follows:You upload your time-series data (e.g. csv) that contains data points (contains arbitrary number of features and y column as the main value) from a timespan T1-T2 (monthly, daily, sub-daily, etc.). We return you the predicted values for the timespan T2-T3Forecasting/prediction might be trained daily or near-realtime fashion.So far we had worked on fast-food transactions...

https://www.youtube.com/watch?v=Tb823aqgX_0

https://github.com/zer0n/deepframeworks/blob/master/README.md

http://hackingdistributed.com/2015/10/14/bitcoin-ng/

https://foursquare.com/infographics/pulse

http://www.apple.com/pr/library/2015/01/27Apple-Reports-Record-First-Quarter-Results.html

https://medium.com/@sarperdag/i-think-i-have-quite-an-interesting-story-f8ddc5e6d4b2

http://www.gatesnotes.com/Books/Why-Inequality-Matters-Capital-in-21st-Century-Review

http://well.blogs.nytimes.com/2014/05/22/is-work-your-happy-place/?hp

https://todoist.com/blog/2014/01/announcing-todoist-next-the-to-do-list-for-lifes-every-moment/

https://www.schneier.com/blog/archives/2013/09/iphone_fingerpr.html

http://blog.lastpass.com/2013/09/lastpass-and-nsa-controversy.html

http://www.apple.com/iphone-5c/

http://benweet.github.io/stackedit/

http://ireport.cnn.com/docs/DOC-981824

http://www.denizyuret.com/2013/05/pitfalls-of-studying-language-in.html

http://hackingdistributed.com/2013/02/11/principled-documentation/

http://hackingdistributed.com/2013/01/29/mongo-ft/