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Forecasting at scale begins with sound statistical learning principles. A deep understanding of the domain and careful feature engineering are essential prerequisites before even attempting to solve a forecasting problem. However, existing methods for incorporating external data often rely on outdated surveys, lagging panel data, and static reports. At MetaLearner, our Exogenous Agent gathers the most up-to-date and validated sources to generate diverse features tailored to each customer’s domain, building a comprehensive and dynamic feature store. This store is leveraged to enhance time-series forecasting through advanced Meta-Learning techniques at MetaLearner.AI.
Each customer and each of their products may vary in requirements and modeling assumptions. Our automated framework intelligently selects the most relevant features from the feature store, integrates trends and sales data from related products, and dynamically assigns an optimized forecasting model stack at the product level. This results in a hyper-personalized forecasting “expert” for each product, one that deeply understands domain-specific trends and sales patterns, with full explainability for the predicted outcomes. These expert models are continuously evaluated and have consistently delivered forecasts that outperform the competition, all while maintaining efficient compute performance thanks to NVIDIA SDKs.
Few vendors have approached forecasting through a truly multi-faceted Meta-Learning framework. In conventional univariate supervised meta-learning, labeling instances using the “best” forecast model often obscures the underlying relationships between individual models. Our solution extends this paradigm by redefining the dependent variable as a multivariate vector of forecast errors. As a result, the meta-model learns the interrelationships among errors across multiple model classes, allowing for more robust and adaptive forecasting.
Our automated meta-learning framework dynamically ensembles models from a pool and evaluates performance across two critical dimensions: [1] forecast error and [2] forecast reliability. It is not only important to know when models are accurate, but also to understand how severe their errors are when they fail. This unique focus enables us to support a wide range of scenarios for our customers, including contingency planning and “doomsday” scenario modeling within MetaLearner’s Operations Research domain.
To sum up, MetaLearner has delivered accurate and realistic forecasting solutions by combining expertly engineered features with advanced statistical learning techniques. The strength of our automated forecasting framework is rooted in the deep expertise of our data scientists and the analytic precision of our ensemble-based models. Where others rely on simplistic, univariate, deterministic models, we offer robust ensemble forecasts enriched by feature-based methods, providing a deeper, more nuanced view of the future and empowering our customers to make better-informed decisions.
