In this webinar recording, you’ll learn from DataRobot’s Chief Scientist Michael Schmidt and Time Series GM Jay Schuren how companies use machine learning to solve critical time series problems like optimizing staffing levels, managing inventory, forecasting future product demand, and more.
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 manually re-built. Until now.
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.
Beyond essential and proven times series methods like ARIMA and Facebook Prophet, DataRobot includes advanced time series models that help you achieve even higher forecasting accuracy.
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.
Time series use cases range from business operations for sales, demand at SKU level, staffing, inventory to a myriad of financial applications.
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.
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.
"Time series machine learning has historically resisted automation. 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."
Professor of Business Administration and Faculty Chair, Harvard Business School
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