Forecasting and financial planning with AI

Day 1: Theory and basics

Introduction to artificial intelligence in finance

• Fundamentals of AI technologies and their use in accounting.
• Trends, developments and innovations in the field of AI-supported financial planning.

Traditional methods vs. AI-supported forecasting

• Comparison of classic forecasting models with AI-based approaches.
• Case studies to illustrate the advantages and disadvantages.

Specific forecasting models for forecasting and financial planning

• Introduction to time series analysis and statistical models:
  - ARIMA (AutoRegressive Integrated Moving Average).
  - SARIMA (Seasonal ARIMA).
  - Exponential smoothing methods and Holt-Winters method.
  - Regression analysis and its application in budget planning.

Machine learning models in forecasting

• Supervised learning: linear and logistic regression, decision trees, random forests.
• Unsupervised learning: clustering methods and their benefits for financial forecasting.

Deep learning and neural networks

• Fundamentals of neural networks and their application in finance.
• RNNs (Recurrent Neural Networks) and LSTM (Long Short-Term Memory) for modeling complex time series.

Day 2: Practice and application

Practical exercises: Working with AI tools

• Introduction to leading AI tools and software solutions for financial planning.
• Data preparation and feature engineering as the basis for precise models.

Hands-on training: model training and optimization

• Setup and training of ARIMA, RNN and LSTM models.
• Optimization of the model parameters and evaluation of the results.
• Application to specific scenarios: Positive and negative cash flows and the burning rate.

Interpretation and presentation of the results

• Analysis of the models created and their forecasting capability.
• Visualization and communication of the results for decision-makers.

Implementation of AI in financial planning

• Strategic planning for the integration of AI in the company.
• Dealing with challenges and change management.

Ethical and legal framework, outlook and future prospects