Real-World Scenarios in Real Time. Before DecisionNext, commodities market analysts spent hours manually collecting data from various sources. Then they’d download it, cleanse it, aggregate it, and generally load it into one or more pre-existing spreadsheets to get a static output. Finally, they top it off with their personal experiences and feelings about the market to create a forecast.
Now, with DecisionNext, you can get real-time recommendations based on machine learning, refined with your unique market insights. You select your data sources with help from DecisionNext industry experts.
Our model identifies the most predictive data, which you approve or modify to produce automated recommendations that continue to improve. It’s science.
Our forecasting and simulation engine,
DN Scenario serves as the knowledge base, collecting and feeding information to DecisionDesk, where it can be acted on.
This application produces recommendations for specific business decisions to help you make tradeoff decisions and monetize your forecast.
The homepage for DN Scenario, Manage Models allows users to run Seeker, the big data tool that searches for predictive relationships between series and automatically builds forecast models, with the ability to view and adjust forecast-model metadata.
View forecasts and historical data, and interact with those forecasts in Explore Scenarios. Enter your opinion about what a value will be in the future and see the effect on related series.
Study Data helps you build new Models to explore the relationships between series before selecting which to include in a Model.
Create new series by applying transformations to one or more existing series.
This module allows users to upload, update, delete, and export data from the database for use outside of DN Scenario.
See which series are being used to forecast a specific series, then modify which series are being used in a forecast, and analyze forecast accuracy.
Cluster Series allows users to group multiple series together to apply parameters to the cluster and make changes to multiple Tune Timeseries predictor models simultaneously.
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