Automated Time Series
Automate the development of sophisticated time series models that predict the future values of a data series based on its history and trend. Organizations of all sizes will improve forecasts for sales volume, product demand by SKU, staffing, inventory, and a host of financial applications.
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 manually re-built. Until now.
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
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.
Getting Value
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
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.
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.