AI & Forecasting

We integrate Machine learning and econometric models for forecasting the evolution of markets and territories. We move beyond the mere description of the present to provide predictive tools capable of evaluating future scenarios, managing uncertainty and optimising strategic resources.
Interested in the Service?
Contact us

Methodological Mix

The meeting of Machine Learning and Statistical Rigour

We do not consider artificial intelligence as a substitute for statistical rigour, but as a necessary methodological extension for managing large volumes of data and complex non-linear relationships.

Our approach combines Machine Learning algorithms (such as Random Forest and Gradient Boosting), ideal for identifying predictive patterns and seasonal dynamics in high-dimensional contexts, with the robustness of’Structured econometrics.

Predictive Accuracy
We are integrating Machine Learning with econometrics to capture non-linear patterns and seasonal dynamics, ensuring reliable forecasts even in complex and uncertain market scenarios.
Local Granularity
Using Small Area Estimation techniques, we produce robust estimates at municipal and sub-provincial levels, overcoming the limitations of small samples and offering a unique spatial view.

Forecasting Solutions

We use artificial intelligence as an extension of statistical rigour to anticipate future trends.. We analyse complex patterns and non-linear relationships to provide scenario simulations that measure the resilience of markets and territories in the face of external shocks.

Machine Learning Integration

Let's combine ensemble algorithms (Random Forest, Gradient Boosting) with structured econometric models.

This mix makes it possible to identify Accurate predictive patterns in the analysis of prices, demand and seasonal dynamics, turning large volumes of data into operational insights.

Granular and Territorial Estimates
Using Small Area Estimation techniques and spatial-temporal imputation models, we produce robust indicators even at the local level. We integrate official data and digital flows to provide reliable sub-provincial estimates, overcoming the limitations of small statistical samples.

Example application: demand forecasting

Machine Learning and Economic Scenarios: The Case of Swedish School Canteens

The project explores the application of machine learning algorithms to optimise the management of school canteens in Sweden.

Through the processing of vast historical datasets, The model provides accurate consumption forecasts and outlines advanced economic impact simulations.. This approach transforms collective catering into a data-driven system, capable of anticipating fluctuations and analysing hypothetical scenarios to improve cost efficiency.

The integration of predictive analytics and simulation scenarios represents an innovative frontier for the public sector, enabling strategic decisions based on scientific evidence and optimal management of food and economic resources on a national scale.

Data-Driven Optimisation
Transform historical data into accurate forecasts to manage resources and costs with maximum efficiency.
Strategic Foresight
It allows decisions to be based on scientific evidence and advanced economic impact simulations.
European Analytical Rigour
Utilising theoretical frameworks and statistical models to compare waste across the 27 EU countries.
Strategic Sustainability
Map waste dynamics to support data-driven decisions and optimise resources.

Application example: resource optimisation

Comparative Statistical Analysis of Food Waste in EU-27 Countries

The project defines theoretical frameworks and applies statistical models for a’Granular comparative analysis of food waste in EU-27 countries.

The study compares different territorial areas to identify inefficiencies and virtuous models.

The combination of solid theoretical foundations and advanced algorithms allows for Map the dynamics of waste on a European scale, providing a detailed overview.

This data-driven analysis is essential for understanding national disparities, measuring the effectiveness of reduction policies, and supporting strategic decisions for more sustainable food resource management within the European Union.

Application example: resource optimisation

Comparative Statistical Analysis of Food Waste in EU-27 Countries

The project defines theoretical frameworks and applies statistical models for a’Granular comparative analysis of food waste in EU-27 countries.

Through the processing of key metrics, such as cumulative costs and demand trends, the study compares the different territorial areas to identify inefficiencies and good practices.

The combination of solid theoretical foundations and advanced algorithms allows for Map the dynamics of waste on a European scale, providing a detailed overview.

This data-driven analysis is essential for understanding national disparities, measuring the effectiveness of reduction policies, and supporting strategic decisions for more sustainable food resource management within the European Union.

Case Studies

Data intelligence in action: our projects

Explore our applied solutions: from food waste reduction models to dynamic price forecasting. We use ensemble algorithms and advanced estimation techniques to solve complex challenges and generate forecasts to high strategic impact.