I'm super excited to introduce modeltime.ensemble, a new time series forecasting package designed to extend modeltime with ensemble methods like stacking, weighting, and averaging.

A eatter way to do your EDA, and with less unnecessary coding and more flexibility using GGPLOT2 + PURRR. When you are plotting different charts during your exploratory data analysis, you sometimes end up doing a lot of repeated coding...

Your company lives off them... Excel files. Why not automate them & save some time? Here's an Excel File you're going to make in this tutorial from R.

The top 5 best articles on R for Business from last month.

Active funds have done poorly over the last ten years, and in most cases, struggled to justify their fees. In the post, there is a supporting chart showing a group of American Funds funds compared to the Vanguard Total Market index.

The drake plan organizes the project work flow according to targets, which are generated by scripts of functions and often functions of functions. The natural flow for our ETL was to check if the raw data was available on the local disc...

Here's a common situation, you have to make a Monday Morning Slide Deck. It's the same deck each week, just date ranges for your data change. Here's how to automate this process with R!

Your company has tons of them - Microsoft Word Documents! Scraping word documents is a powerful technique for extracting data. Let's learn how with R, officer, & tidyverse.

Modeltime unlocks time series models and machine learning in one framework.

We've crafted an amazing course to teach Data Scientists and Business Analysts how to make high-performance time series forecasts! We've combined an innovative program with a clear-cut path to forecasting using feature engineeirng, machine learning, and deep learning! You'll undergo a complete transformation. Time to accelerate your career!

Anomaly detection is the process of identifying items or events in data sets that are different than the norm. Anomaly detection is an important part of time series analysis: (1) Detecting anomalies can signify special events, and (2) Cleaning anomalies can improve forecast error.