Business Science EARL SF 2017 Presentation: tidyquant, timekit, and more!

The EARL SF 2017 conference was just held June 5 – 7 in San Francisco, CA. There were some amazing presentations illustrating how R is truly being embraced in enterprises. We gave a three-part presentation on tidyquant for financial data science at scale, timekit for time series machine learning, and Business Science enterprise applications. We’ve uploaded the EARL presentation to YouTube. Check out the presentation, and don’t forget to check out our announcements and to follow us on social media to stay up on the latest Business Science news, events and information! If you’re interested in financial analysis, forecasting, and business applications, check out our 30 minute presentation from EARL SF 2017! The presentation is three-in-one: Financial data science at scale with tidyquant (0:45) Time series machine learning with timekit (9:10) Enterprise applications with Business Science (23:00)
Original Post: Business Science EARL SF 2017 Presentation: tidyquant, timekit, and more!

tidyquant: R/Finance 2017 Presentation

The R/Finance 2017 conference was just held at the UIC in Chicago, and the event was a huge success. There were a ton of high quality presentations really showcasing innovation in finance. We gave a presentation on tidyquant illustrating the key benefits related to financial analysis in the tidyverse. We’ve uploaded the tidyquant presentation to YouTube. Check out the presentation. Don’t forget to check out our announcements and to follow us on social media to stay up on the latest Business Science news, events and information! If you’re interested in financial analysis, check out our short 6 minute presentation from R/Finance 2017 that discusses the tidyquant package benefits. The discussion touches on the current state of financial analysis, answers the question “Why tidyquant?”, discusses the core tq functions along with tq benefits including financial data science at scale.
Original Post: tidyquant: R/Finance 2017 Presentation

timekit: New Documentation, Function Improvements, Forecasting Vignette

We’ve just released timekit v0.3.0 to CRAN. The package updates include changes that help with making an accurate future time series with tk_make_future_timeseries() and we’ve added a few features to tk_get_timeseries_signature(). Most important are the new vignettes that cover both the making of future time series task and forecasting using the timekit package. If you saw our last timekit post, you were probably surprised to learn that you can use machine learning to forecast using the time series signature as an engineered feature space. Now we are expanding on that concept by providing two new vignettes that teach you how to use ML and data mining for time series predictions. We’re really excited about the prospects of ML applications with time series. If you are too, I strongly encourage you to explore the timekit package important links below. Don’t forget…
Original Post: timekit: New Documentation, Function Improvements, Forecasting Vignette