We believe that, especially in dynamic markets, start-ups and SMEs need tools that are not only data-driven, but also create real added business value.
How a Modern Forecasting Engine Paves the Way for Data-Driven Decisions
What if you not only analyzed your company data - but also used it to make reliable predictions for tomorrow?
Photo from Jon Tyson on Unsplash
Effect: Scalable Forecasts for Genuine Planning Security
In a data-driven market environment, precise forecasts are increasingly crucial to economic success. For a current customer project, we have developed a modular, AI-supported forecasting engine that makes exactly that possible: reliable time series forecasts that can be customized and used in a wide range of industries.
The aim: to provide the customer with a solution that can be used to proactively identify and manage demand trends, operational processes or unusual developments, for example - with minimal effort and maximum impact.
Challenge: lots of data, but no forecasting capability
Our customer already had access to extensive time series data - for example from sales, production and logistics. The challenge was to prepare and model this data in such a way that it could be automatically converted into reliable forecasts.
It was not just about accuracy, but also about scalability, modularity and easy integration into existing processes. The solution had to be sustainable in the long term, but also deliver results in the short term - without any costly system migrations or high operating costs.
Solution: A forecasting platform that grows with your requirements
For the project, we developed a prediction engine based on NeuralProphet - a deep learning framework based on PyTorch and specially designed for time series analyses. The platform combines modern AI methods with lean data architecture. In detail, we have implemented the following elements:
Flexible model architecture
The models were designed in such a way that they can map trend and seasonality components, autoregressive correlations, external influencing factors and customer-specific events. This results in forecasts that are not only mathematically correct, but also relevant to the business.
Automated performance optimization
In order to exploit the full potential of the models, we used Optuna for hyperparameter tuning. This ensures reproducible results and makes it possible to systematically compare and improve model variants.
Lightweight, integrable data infrastructure
In the backend, we built a lean data architecture based on SQLite and SQLAlchemy. This was complemented by an automated pipeline for converting and migrating Excel data into structured SQL tables - including versioning and validation. This enabled us to seamlessly integrate the existing databases.
Reusability for different applications
The solution has a modular structure and can be flexibly applied to different scenarios - from demand forecasting and production planning to early warning in the event of anomalies.
Conclusion: AI that adapts to your reality
This project shows how modern machine learning technologies can be combined with efficient data engineering to create real added value - not in the lab, but in practice.
If you are faced with the challenge of making better use of complex time series data, the path to a solution is often shorter than you think.
We are happy to help you build a forecasting platform that speaks your data - and helps you make informed decisions.
