Learn Timeseries

I'm super excited to introduce the new Modeltime Backend for Spark. Let's use it to perform forecasting with tidymodels.

I'm super excited to introduce the experimental feature for performing iterative forecasting.

I'm super excited to introduce the new panel data forecasting functionality in modeltime. It's perfect for making many forecasts at once without for-loops.

A gentle introduction to our forecasting package, Modeltime. Modeltime extends the Tidymodels ecosystem for time series forecasting. Learn how to forecast with ARIMA, Prophet, and linear regression time series models.

I'm super excited to introduce the new parallel processing functionality in modeltime. It's perfect for speeding up hyperparameter tuning of forecast models using parallel processing.

I'm super excited to introduce the new autoregressive forecast functionality in modeltime that allows you to convert any tidymodels regression algorithm into an autoregressive forecasting algorithm.

I'm super excited to introduce modeltime.h2o, an H2O AutoML backend for forecasting

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.

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.

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!

Seasonality is the presence of variations that occur at specific regular intervals, such as weekly, monthly, or quarterly. Seasonality can be caused by factors, such as weather or holiday, and consists of periodic and repetitive patterns in a time series.

A collection of tools for working with time series in R Time series data wrangling is an essential skill for any forecaster. timetk includes the essential data wrangling tools.

I'm so excited to introduce modeltime, a new time series forecasting package designed to integrate tidymodels machine learning packages into a streamlined workflow for tidyverse forecasting.

The 2nd part in our Time Series in 5-minutes article series. Learn how to visualize autocorrelations and cross correlation.

The 1st part in our Time Series in 5-minutes article series. Learn how to visualize time series with the time plot.

Timetk Version 2.0.0 has just been released. Here's what's new for time series data analysis.

Machine learning is a powerful way to forecast Time Series. Feature Engineering is critical. A new innovation is coming in timetk - to help generate 200+ time-series features.

We can often improve forecast performance by cleaning anomalous data prior to forecasting. This is the perfect use case for integrating the clean_anomalies() function from anomalize into your forecast workflow.

This article demonstrates a real-world case study for business forecasting with regression models including artificial neural networks (ANNs) with Keras

KERAS LSTM deep learning time series analysis. Use the NASA sunspots data set to predict sunspots ten years into the future with an KERAS LSTM deep learning model.

Information Security (InfoSec) is critical to a business. For those new to InfoSec, it is the state of being protected against the unauthorized use of information, especially electronic data. A single malicious threat can cause massive damage to a firm, large or small. It’s this reason when I (Matt Dancho) saw Russ McRee’s article, “Anomaly Detection & Threat Hunting with Anomalize”, that I asked him to repost on the Business Science blog. In his article, Russ speaks to use of our new R package, anomalize, as a way to detect threats (aka “threat hunting”). Russ is Group Program Manager of the Blue Team (the internal security team that defends against real attackers) for Microsoft’s Windows and Devices Group (WDG), now part of the Cloud and AI (C+AI) organization. He writes toolsmith, a monthly column for information security practitioners, and has written for other publications including Information Security, (IN)SECURE, SysAdmin, and Linux Magazine. The data Russ routinely deals with is massive in scale: He processes security event telemetry of all types (operating systems, network, applications, service layer) for all of Windows, Xbox, the Universal Store (transactions/purchases), and a few others. Billions of events in short order.

We are ready to demo our new experimental package for Algorithmic Trading, flyingfox, which uses reticulate to to bring Quantopian’s open source algorithmic trading Python library, Zipline, to R. The flyingfox library is part of our NEW Business Science Labs innovation lab, which is dedicated to bringing experimental packages to our followers early on so they can test them out and let us know what they think before they make their way to CRAN. This article includes a long-form code tutorial on how to perform backtest optimizations of trading algorithms via grid search and parallel processing. In this article, we’ll show you how to use the combination of tibbletime (time-based extension of tibble) + furrr (a parallel-processing compliment to purrr) + flyingfox (Zipline in R) to develop a backtested trading algorithm that can be optimized via grid search and parallel processing. We are releasing this article as a compliment to the R/Finance Conference presentation “A Time Series Platform For The Tidyverse”, which Matt will present on Saturday (June 2nd, 2018). Enjoy!

Learn time series analysis with Keras LSTM deep learning. Learn to predict sunspots ten years into the future with an LSTM deep learning model.

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.

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.

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 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()!

We’ve got some good stuff cooking over at Business Science. Yesterday, we had the fifth official release (0.5.0) of tidyquant to CRAN. The release includes some great new features. First, the Quandl integration is complete, which now enables getting Quandl data in “tidy” format. Second, we have a new mechanism to handle selecting which columns get sent to the mutation functions. The new argument name is… select, and it provides increased flexibility which we show off in a rollapply example. Finally, we have added several PerformanceAnalytics functions that deal with modifying returns to the mutation functions. In this post, we’ll go over a few of the new features in version 5.

Today I’m very pleased to introduce the new Quandl API integration that is available in the development version of tidyquant. Normally I’d introduce this feature during the next CRAN release (v0.5.0 coming soon), but it’s really useful and honestly I just couldn’t wait. If you’re unfamiliar with Quandl, it’s amazing: it’s a web service that has partnered with top-tier data publishers to enable users to retrieve a wide range of financial and economic data sets, many of which are FREE! Quandl has it’s own R package (aptly named Quandl) that is overall very good but has one minor inconvenience: it doesn’t return multiple data sets in a “tidy” format. This slight inconvenience has been addressed in the integration that comes packaged in the latest development version of tidyquant. Now users can use the Quandl API from within tidyquant with three functions: quandl_api_key(), quandl_search(), and the core function tq_get(get = "quandl"). In this post, we’ll go through a user-contributed example, How To Perform a Fama French 3 Factor Analysis, that showcases how the Quandl integration fits into the “Collect, Modify, Analyze” financial analysis workflow. Interested readers can download the development version using devtools::install_github("business-science/tidyquant"). More information is available on the tidyquant GitHub page including the updated development vignettes.