Facebook'un çekirdek veri bilimi ekibi tarafından hazırlanan; açık kaynak, zaman serisi veri tahminleme (time series data forecasting) kütüphanesi Prophet'in yeni versiyonu (0.6) yayınlandı.
Kod: https://t.co/rz6o4GOaud
Makale: https://t.co/gEdYdFuMZw
@rob_rix Hierarchical modeling works for this. See for example @seanjtaylor 's Prophet writeup https://t.co/m1iHIKwZpq and @mcmc_stan implementation https://t.co/2vaS5Sx1gV
@seanjtaylor & Benjamin Letham describe why & how @facebook’s #DataScience team developed #OpenSource Prophet to help analysts #forecast #timeseries at scale. @fbOpenSource
https://t.co/BC8aByroRh
https://t.co/DioUvGVF1l
Day 2: Went to a @ps_python meetup discussing time series forecasting at scale with FB Prophet so I thought I would do an implementation using it. Used it on a Chicago crime dataset.
Read the paper here: https://t.co/EsR4FftfvI
#100DaysOfCode #100DaysOfMLCode https://t.co/zVB1RTO1gM
@willyhakim @_bletham_ Thank you! Not sure when I'll have time for a more thorough write-up, but you can read our PeerJ paper, which is a decent explanation: https://t.co/5J02dNm6l5
If you’d like to effectively tackle a forecasting problem quickly, check out Facebook’s prophet: https://t.co/CwCHNnCMWZ, paper here: https://t.co/ZtAmPTRgk7