Market Overview

DataRobot Announces Automated Time Series Solution that Allows Frontline Business People to Predict the Future


New Offering Solves Time-Dependent Business Problems with Nutonian's
Eureqa Forecasting Technology and DataRobot's Automated Machine Learning

the pioneering architects of automated machine learning, today announced
the general availability of DataRobot Time Series. Following an
extensive collaboration with more than 75 customers and world-class data
scientists, this latest breakthrough completely automates sophisticated
time series modeling. Finally, frontline business people can quickly
build highly accurate forecasts for a diverse array of time-dependent
prediction opportunities.

Until now, handling time-dependency modeling required highly specialized
expertise to even set up the problem correctly. Conventional modeling
uses randomly selected records from a dataset to build and evaluate
predictive models. For example, a representative sample of loan records
and whether a customer defaulted. Each record is relatively similar.

Not so with time series. Temporal relationships matter. And there are so
many potential ones to consider. Do you want to predict demand for a
product three days from now? What day of the week is that? How does
demand on that day of the week vary with demand on the other days? For
the same day in previous weeks? Where does the week fall within the
month? Does that matter? How about within the quarter? How is this month
and this quarter comparing so far to last month and last quarter? The
same month and quarter a year ago? Two years through 10 years ago? Is
that a holiday in any location of interest and how does holiday
performance differ? What promotions have you run and when did you run
them? How have such promotions performed under similar temporal

Building on the 2017 acquisition
of Nutonian, Inc
. and its proven Eureqa modeling engine, DataRobot
Time Series understands all these questions and how to set up the
problem based on the answers. Then the DataRobot automation platform
constructs and evaluates hundreds to thousands of different time series
models and scores their performance – taking into account all the
different temporal conditions to determine real world accuracy.

DataRobot Time Series beta customers, including Fortune 2000 retailers,
banks, and hospital networks, have quickly built accurate models for
staffing, inventory management, demand forecasting, financial
applications, and more – all without the need for manual forecasting,
specialized data science expertise, and custom coding.

"Forecasting underpins most critical business functions. If you can
predict the future, you can usually win the game. But it is one of the
hardest problems in data science. Since the Nutonian acquisition last
May, we've been on a massive undertaking to combine Nutonian and
DataRobot innovations into the best time series product in the world.
This fourth version, which has been extensively tested by customers in
production, automates a wide array of advanced best practices in areas
like feature engineering and thereby achieves a whole new level of
accuracy," said Michael Schmidt, Chief Scientist, DataRobot.

This new version, which is available now, includes advanced machine
learning models for forecasting, as well as essential time series
methods like ARIMA and Facebook Prophet. Full API support helps AI
engineers integrate modeling and prediction directly into business
processes and applications.

"Time series machine learning has historically resisted automation,"
says Srikant Datar, Professor of Business Administration and Faculty
Chair of the Harvard Innovation Lab at Harvard Business School. "Having
worked with DataRobot's Time Series product for the past several months,
including delivering real financial applications, I'm amazed at what is
possible and how easily models can be built."

Steward Health Care, the largest for-profit private hospital operator in
the United States, is using DataRobot to significantly improve
operational efficiency and reduce costs among their network of 38
hospitals across the nation. Sixty percent of hospital operational
expenses come from staffing alone. With DataRobot's improved forecasts
for patient volume, Steward's potential labor savings amount to $2
million by reducing hospital overstaffing by 1 percent for eight of the
38 hospitals in Steward's network.

"We have data – a lot of data – and we want to use it to our advantage,"
said Erin Sullivan, executive director of information systems and
software development at Steward Health Care. "DataRobot has the tools to
help us take historical data, manipulate it, and learn from it. We've
already experienced tremendous cost and time savings with DataRobot, and
these latest advancements will further transform how we forecast nurse
staffing and patients' length of stay—both of which will yield
significant benefits for our hospital network."

To learn more about DataRobot Time Series and the product's modeling and
forecasting capabilities, visit
or join a webinar, "Using
AI and Time Series Machine Learning Methods to Improve Demand
Forecasting Models
" on Thursday, September 6th at 1:00 pm ET.

About DataRobot
DataRobot offers an enterprise machine
learning platform that empowers users of all skill levels to develop and
deploy machine learning and AI faster. Incorporating a library of
hundreds of the most powerful open source machine learning algorithms,
the DataRobot platform automates, trains, and evaluates models in
parallel, delivering AI applications at scale. DataRobot provides the
fastest path to AI success for organizations of all sizes. For more
information, visit

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