In today’s financial forecasting, traditional methods like manual calculations and relying on expert
intuition often don’t meet the needs of complex industrial settings, such as Siemens’s Air-Insulated
Switchgear Business.
This thesis explores how machine learning algorithms can improve predictive financial modeling,
especially for budgeting and forecasting key financial metrics. Although only 3-4 years of historical
data are available, this research looks into machine learning techniques that can still make accurate
predictions by uncovering patterns in the data. The study focuses on time series models, regression
techniques, and ensemble methods that are effective for small datasets, and assesses their ability to
forecast financial KPIs. Additionally, the research examines which financial metrics most influence
forecasting accuracy, aiming to develop a more reliable, data-driven approach to financial planning
that can evolve and enhance organizational decision-making.
Siemens AG provides the dataset and contains tabular time series data.
Research Objectives
1. Assess the effectiveness of different algorithms in forecasting financial KPIs. Focus on determining
which algorithm provides the most accurate predictions and best captures the
non-linearity’s in the data.
2. Exploring different techniques to generate synthetic data points and various data augmentation
techniques to enhance the robustness of the predictive models.
3. Experiment with both unified and factory-specific models to identify whether a single model
can effectively forecast across all factories or if individual models for each factory yield better
results.
4. Implement and assess the impact of regularization, ensemble methods, and other advanced
techniques on the performance of the predictive models.
The thesis involves the following key steps:
• Step 1: Literature review and theoretical framework development.
• Step 2: Data pre-processing, and analysis.
• Step 3: Design and develop machine learning model architectures tailored to forecast financial
KPIs.
• Step 4: Evaluate and compare the results of the models.
• Step 5: Select the best performing model and refining it further.
• Step 6: Thesis writing and final presentation preparation.
Through an in-depth exploration of data analytics and machine learning, this thesis seeks to elevate
predictive financial modeling by investigating effective strategies and model architectures. The
theoretical framework, grounded in a comprehensive literature review, will guide the study’s key
steps, leading to actionable insights for improving budget planning and financial forecasting in
Siemens’ AIS-producing factories.
References
[1] Samuel A Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E Tillman, Prashant Reddy, and
Manuela Veloso. Generating synthetic data in finance: opportunities, challenges and pitfalls.
In Proceedings of the First ACM International Conference on AI in Finance, pages 1–8, 2020.
[2] Daniel Broby. The use of predictive analytics in finance. The Journal of Finance and Data
Science, 8:145–161, 2022.
[3] Odeyemi Olubusola, Noluthando Zamanjomane Mhlongo, Donald Obinna Daraojimba, Adeola
Olusola Ajayi-Nifise, and Titilola Falaiye. Machine learning in financial forecasting: A us
review: Exploring the advancements, challenges, and implications of ai-driven predictions in
financial markets. World Journal of Advanced Research and Reviews, 21(2):1969–1984, 2024.
[4] Meryem Ouahilal, Mohammed El Mohajir, Mohamed Chahhou, and Badr Eddine El Mohajir. A
comparative study of predictive algorithms for business analytics and decision support systems:
Finance as a case study. In 2016 International Conference on Information Technology for
Organizations Development (IT4OD), pages 1–6. IEEE, 2016.