Predictive sales analytics to predict product backorders can increase sales and customer satisfaction. Using a Kaggle dataset, we use H2O AutoML predict backorders.
Today we are introducing
tibbletime v0.0.2, and we’ve got a ton of new features in store for you. We have functions for converting to flexible time periods with the
~period formula~ and making/calculating custom rolling functions with
rollify() (plus a bunch more new functionality!). We’ll take the new functionality for a spin with some weather data (from the
weatherData package). However, the new tools make
tibbletime useful in a number of broad applications such as forecasting, financial analysis, business analysis and more! We truly view
tibbletime as the next phase of time series analysis in the
tidyverse. If you like what we do, please connect with us on social media to stay up on the latest Business Science news, events and information!
We are very excited to announce the initial release of our newest R package,
tibbletime. As evident from the name,
tibbletime is built on top of the
tibble package (and more generally on top of the
tidyverse) with the main
purpose of being able to create time-aware tibbles through a one-time
specification of an “index” column (a column containing timestamp information). There are a ton of useful time functions that we can now use such as
time_collapse(). We’ll walk through the basics in this post.
We’re excited to announce the
alphavantager package, a lightweight R interface to the Alpha Vantage API! Alpha Vantage is a FREE API for retreiving real-time and historical financial data. It’s very easy to use, and, with the recent glitch with the Yahoo Finance API, Alpha Vantage is a solid alternative for retrieving financial data for FREE! It’s definitely worth checking out if you are interested in financial analysis. We’ll go through the
alphavantager R interface in this post to show you how easy it is to get real-time and historical financial data. In the near future, we have plans to incorporate the
tidyquant to enable scaling from one equity to many.
Tidy Time Series Analysis, investigate lags and autocorrelation to understand seasonality and form the basis for autoregressive forecast models.
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.
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
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.
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
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.
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 stay up on the latest Business Science news, events and information!
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
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
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!