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.
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.