Hi I work mostly in data engineering and trying to promote use of sparkR within the company I recently joined. Some of the users are working around forecasting a bunch of things and want to use SparklyR as they found time series implementation is better than SparkR.
Does anyone have a point of view regarding this? Is SparklyR is better than SparkR in certain use cases? On Thu, Sep 20, 2018 at 4:07 AM, Mina Aslani <aslanim...@gmail.com> wrote: > Hi, > > Thank you for your quick response, really appreciate it. > > I just started learning TimeSeries forecasting, and I may try different > methods and observe their predictions/forecasting.However, my > understanding is that below methods are needed: > > - Smoothing > - Decomposing(e.g. remove/separate trend/seasonality) > - AR Model/MA Model/Combined Model (e.g. ARMA, ARIMA) > - ACF (Autocorrelation Function)/PACF (Partial Autocorrelation Function) > - Recurrent Neural Network (LSTM: Long Short Term Memory) > > Kindest regards, > Mina > > > > On Wed, Sep 19, 2018 at 12:55 PM Jörn Franke <jornfra...@gmail.com> wrote: > >> What functionality do you need ? Ie which methods? >> >> > On 19. Sep 2018, at 18:01, Mina Aslani <aslanim...@gmail.com> wrote: >> > >> > Hi, >> > I have a question for you. Do we have any Time-Series Forecasting >> library in Spark? >> > >> > Best regards, >> > Mina >> > -- Best Regards, Ayan Guha
