FORETHINKER
Art is connecting the dots
The Forethinker is a result of endless passion about forecasting and predictive modeling. We believe that there is no noise in the data and that nothing in this world is random. We dive deep to examine each signal in the data and engineer the features. Our models estimate future value, risk, and how that risk can be controlled.It is important to emphasize that all the models are at bottom just the tool for approximate thinking. Tool that helps us to transform our intuition about the future values.
Lastly, we all like simplicity but it is important to remember that our models are simple, not the world. Our goal is to close the gap and without cutting off all the important features, and simplify the world, capture the features and engineer the best possible solution.
Our models are accurate to more than 4 decimal places
Forecasting
Scenario Planning
Predictive Modeling
Price Optimization
E-Commerce
Thanks to the application of operations research techniques, it is possible to give accurate, optimal answers to complex problems with a large number of variables and constraints while ensuring that the maximum and minimum goals defined in the problem are reached. Work with all the advanced analysis techniques to add objectivity and quality to your business strategy.
ML models are capable of identifying behavioral patterns and extracting knowledge that will allow you to anticipate, forecast, and control future outcomes
By applying ML techniques, it is possible to identify the most suitable decisions by seeking the equilibrium point among multiple objectives
Most of the simple statistical models heavily rely on averaging and overgeneralizing data. By doing this it often masks the important features at the cost of models' predictive strength
ML techniques allow us to develop solutions capable of understanding, learning and recognizing hidden patterns in data in many different formats
Predictive Modeling
and Optimization
Shows how well the model performs with all possible different thresholds (useful in comparing models), and trade-off between sensitivity and specificity. The size of the area indicates how well the model�s scoring does at separating 2 classes (bigger is better)
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milan@theforethinker.com