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Automated Time Series

Automate the development of sophisticated time series models that predict the future values of products based on trends and their history. Organizations of all sizes will improve forecasts for sales volume, product demand by SKU, staffing, inventory, and a host of financial applications.

In this webinar recording, you’ll learn from DataRobot’s Chief Scientist Michael Schmidt and Automated Time Series General Manager Jay Schuren how companies use machine learning to solve critical time series problems, such as optimizing staffing levels, managing inventory, forecasting future product demand.

Watch the on-demand time series webinar

Time Series Modeling

The goal of time series modeling is to predict future performance from past behavior – such as forecasting sales over a holiday season, predicting how much staff you need for the upcoming week, or ensuring inventory meets manufacturing demands without overstocking.

Unfortunately, time series modeling can be a complex and laborious process, because many historical events can impact the current predictions and finding the most influential signals is difficult. As the environment changes, such as after introducing a new product or a competitor opening a new store, these models need to be rebuilt manually. Until now.

Accelerating AI Impact with Time Series Automation

DataRobot integrates best practices in time series modeling, including automating time series feature engineering to discover predictive signals. It also automatically detects stationarity, seasonality, transforms the target, and implements backtesting to achieve the highest possible accuracy.

High Accuracy from Model Diversity

Beyond essential and proven time series methods like ARIMA and Facebook Prophet, DataRobot includes advanced time series models that help you achieve even higher forecasting accuracy.

Ready for the Real World

Since the goal of a time series model is to both extract understanding and predict future outcomes, DataRobot offers many ways to visualize insights over time and to deploy models to production, including full API support to integrate modeling into business processes and applications.

Automated Time Series In Action

Automated time series use cases range from business operations for sales, demand at SKU level, staffing, inventory to a myriad of financial applications.

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 operations 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% for eight of the 38 hospitals in Steward’s network.

Download Case Study
We have data – a lot of data – and we want to use it to our advantage. 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.
Erin Sullivan

Executive Director, Steward Health Care

What people say
  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

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