The Intersection of AutoML and Multi-Objective Optimization: Revolutionizing Decision-Making with AI-Powered Forecasts

Spotting trends, predicting demand, and making forecasts are crucial elements of a decision-making process. An intelligent synthesis of AutoML and Multi-Objective Optimization brings forecasting to a new level.

We're hiring!
September 29, 2023
minutes read
Data Science

At the heart of business decision-making, spotting trends, predicting demand, and making forecasts is a complex yet vital process. Like aiming at a bullseye in dart-throwing, precision matters. The key to achieving this precision in time-series forecasting is the intelligent synthesis of AutoML and Multi-Objective Optimization.

Let AutoML do the Heavy Lifting

Imagine you have a stack of potential Machine Learning (ML) models to apply to a time-series forecasting problem. How do you select the best ML model for your problem? You could manually tune each model's parameters and pit them against one another. But this process can be time-consuming.

This is where AutoML comes to the rescue. It has the potential to pick and tune the best model for your specific problem automatically, thus, saving effort and increasing efficiency. By comparing the optimised models obtained through AutoML, you can select the one performing best, ensuring enhanced prediction performance.

The Complexity of Heterogenous Data

While implementing Machine Learning isn't a magic wand, complexities arise with the diversity of data, which is often heterogeneous. How can you improve your prediction performance? The answer lies in ABC classification and Ensembling.

Different models work best for diverse data segments. Statistical models, for example, excel with regular patterns, while ML models like deep learning work better with irregular patterns caused by factors like weather or promotions. Employing an ensemble of these models can thus give you optimum prediction performance.

Optimizing the Optimization: Know When To Stop

We all love precision. But how long should you optimize? The scale of accuracy and business impact starts tipping after a certain point. The cost for every notch of additional accuracy can become significantly larger, causing its business value to shrink.

Mapping the business impact to additional optimization can provide context to the trade-off between increased accuracy and cost. This understanding aids in making a strategic decision on when to stop optimizing.

A Transformative Approach: Multi-objective Optimisation

The crux of boosting business impact doesn't stop at optimizing accuracy. It's about understanding and managing uncertainty - ensuring your business gains the most while risk is minimised. In other words, it's about the strategic trade-off between the marginal cost and the marginal benefit.

Understanding the uncertainties involved and including them as a factor in optimisation provides a path to maximize business value. That's where Multi-Objective Optimization comes into play. This approach helps you strike the perfect balance between the marginal cost and benefit, leading to a significant return on investment.

Concluding Thoughts

Embracing the intersection of AutoML and Multi-Objective Optimization is a transformative step towards data-informed decisions in time-series forecasting. By navigating complexities, increasing accuracy, and managing risks, your business can stay ahead of the curve and bolsters its competitiveness.

At paretos, we are revolutionising decision-making by providing optimized forecasts enabling complex decisions to be made in real-time. We leverage ML in our timeseries forecasts to ensure your business stays well-informed and ready to tackle unpredictability.

We are the leading AI-based decision intelligence platform for effective, data-driven decision-making processes in companies. No more bad decisions!
Robert Haase
AI Scientist
As a physicist always seek to find well-suited explanations and modelling approaches for complex mechanisms. Actively working with various KMUs and international companies in the field of forecasting and demand planing enabled me to generate maximal business value with the help of state-of-the-art machine learning approaches.
Fabian Rang
CTO & Co-Founder
My passion lies in mathematical methods for processing complex data structures and models. Traded my engineering job for a PhD in Systems Engineering and Mathematics and spent several years researching efficient optimization modeling.

Related articles

Untapped potential
Customer examples
Software demo

Book a demo

Would you like to know if paretos is the right solution for you?
Feel free to schedule a non-binding consultation appointment.