Financial forecasting on the basis of time series analyses for an energy company

In the past, a client was faced with the challenging task of significantly optimising the forecasting accuracy of its business management corporate functions. This was especially true for the critical area of Financial Forecasting. It was not only important to improve accuracy, but also to improve the entire forecasting process to automate for future cost forecasting. This automation should provide more time and resources for the company's analyses.

Based on our expertise in data analysis and artificial intelligence, we have developed an individual solution: a Automated forecasting pipeline. This innovative solution consisted of several integrated modules:

• Data preparation: By using advanced data processing techniques, the client's data was optimised and prepared for the next step.
• Training of forecasting models: Using more than 10 different models, including classical statistical approaches as well as modern deep learning methods, a comprehensive analysis was carried out to select the optimal forecasting method for the client.
• Model selection by means of ranking module: Here, an intelligent ranking system selected the best model that best matched the client's specific requirements and data.
• Cost forecastThis pipeline enabled both monthly and annual financial forecasting.
• User-friendly dashboards: Intuitive dashboards were developed for better communication between technical and non-technical stakeholders. These dashboards visualised both forecast data and key performance indicators and were accessible to both the business team and the development team.

Our solution optimised the client's approach to cost forecasting. Where previously controllable cost forecasts were primarily based on management's subjective estimates, our data-driven forecast now a objective verification. This has significantly improved the reliability and accuracy of the predictions.

In fact, the automated financial forecast we developed for the client's corporate functions showed an Significant improvement of the financial statements compared to their previous manual methods. In addition, the ability to visually display historical costs provided a sound basis for internal discussions and strategic decisions. It was a powerful example of how modern data science and AI technologies can transform business operations.

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