Tidy Time Series Analysis, Part 3: The Rolling Correlation

In the third part in a series on Tidy Time Series Analysis, we’ll use the runCor function from TTR to investigate rolling (dynamic) correlations. We’ll again use tidyquant to investigate CRAN downloads. This time we’ll also get some help from the corrr package to investigate correlations over specific timespans, and the cowplot package for multi-plot visualizations. We’ll end by reviewing the changes in rolling correlations to show how to detect events and shifts in trend. If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance. If interested, contact us. If you haven’t checked out the previous two tidy time…
Original Post: Tidy Time Series Analysis, Part 3: The Rolling Correlation

BizSci Package Updates: Formerly timekit… Now timetk :)

We have several announcements regarding Business Science R packages. First, as of this week the R package formerly known as timekit has changed to timetk for time series tool kit. There are a few “breaking” changes because of the name change, and this is discussed further below. Second, the sweep and tidyquant packages have several improvements, which are discussed in detail below. Finally, don’t miss a beat on future news, events and information by following us on social media. The timetk package (formerly timekit) is a relatively new package that is aimed at assisting users with working with time series in R. It helps users switch back and forth between time based “tibbles” (tidy data frames with dates or date times) and the other time series objects in R (xts, zoo, ts, etc). Equally important, timetk includes functions that…
Original Post: BizSci Package Updates: Formerly timekit… Now timetk 🙂

Tidy Time Series Analysis, Part 2: Rolling Functions

In the second part in a series on Tidy Time Series Analysis, we’ll again use tidyquant to investigate CRAN downloads this time focusing on Rolling Functions. If you haven’t checked out the previous post on period apply functions, you may want to review it to get up to speed. Both zoo and TTR have a number of “roll” and “run” functions, respectively, that are integrated with tidyquant. In this post, we’ll focus on the rollapply function from zoo because of its flexibility with applying custom functions across rolling windows. If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance.…
Original Post: Tidy Time Series Analysis, Part 2: Rolling Functions

sweep: Extending broom for time series forecasting

We’re pleased to introduce a new package, sweep, now on CRAN! Think of it like broom for the forecast package. The forecast package is the most popular package for forecasting, and for good reason: it has a number of sophisticated forecast modeling functions. There’s one problem: forecast is based on the ts system, which makes it difficult work within the tidyverse. This is where sweep fits in! The sweep package has tidiers that convert the output from forecast modeling and forecasting functions to “tidy” data frames. We’ll go through a quick introduction to show how the tidiers can be used, and then show a fun example of forecasting GDP trends of US states. If you’re familiar with broom it will feel like second nature. If you like what you read, don’t forget to follow us on social media to…
Original Post: sweep: Extending broom for time series forecasting

Tidy Time Series Analysis, Part 1

In the first part in a series on Tidy Time Series Analysis, we’ll use tidyquant to investigate CRAN downloads. You’re probably thinking, “Why tidyquant?” Most people think of tidyquant as purely a financial package and rightfully so. However, because of its integration with xts, zoo and TTR, it’s naturally suited for “tidy” time series analysis. In this post, we’ll discuss the the “period apply” functions from the xts package, which make it easy to apply functions to time intervals in a “tidy” way using tq_transmute()! An example of the visualization we can create using the period apply functions with tq_transmute(): We’ll primarily be using two libraries today. library(tidyquant) # Loads tidyverse, tidquant, financial pkgs, xts/zoo library(cranlogs) # For inspecting package downloads over time As you can tell from my laptop stickers, I’m a bit of a tidyverse fan. 🙂…
Original Post: Tidy Time Series Analysis, Part 1

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