Time series forecasting allows businesses to gain insights into market trends and plan production, storage, and sales in advance. There is a shift towards applying machine learning (ML) models for forecasting on hierarchical sales datasets. However, large historical training data is not always available, particularly in markets where sales are not frequent but rather occur weekly or monthly. In such cases, current ML approaches often require years of training data. This study focuses on small datasets and evaluates the potential of ensemble methods to improve prediction accuracy. Forecasting models are assessed on three hierarchical sales datasets from two companies. Sixteen traditional statistical and ML models were evaluated using customized test periods for each dataset. The results show that the best-performing model depends on the dataset's characteristics. A seasonal naive model showed effective seasonality detection, while a baseline model robustly outperformed complex models in one of the datasets. Among traditional statistical models, theta and exponential smoothing produced similar results, and a statistical ensemble outperformed in larger prediction horizons. ML models showed mixed results, with ML ensembles showing significant advantages on the largest dataset. The study demonstrates the importance of model choice relative to dataset specifics, with traditional statistical approaches often outperforming their machine-learning counterparts on small datasets.

The increasing size and complexity of datasets, the need for constant adaptation to current conditions, and the potential benefits of machine learning (ML) techniques, such as flexibility and the ability to incorporate additional features, have led to the increasing use of ML techniques in forecasting as an alternative to traditional statistical methods. However, the results are often not transferable to smaller datasets. This paper analyses a real inventory management dataset and compares statistical and ML methods to determine which techniques consistently produce accurate results, even for smaller datasets. The results show that the choice of aggregation level affects the performance of statistical and ML methods, with the LightGBM model showing consistent performance across different scenarios and aggregation levels, and simpler methods effectively modelling intermittent or lumpy time series.

In the contribution at hand, multilayer feedforward neural networks based on multi-valued neurons (MLMVN) are applied on a classification problem in the context of cyber-physical systems. MLMVN are a specific type of complex valued-neural networks. The aim is to apply MLMVN on a benchmark dataset and to classify individual states of a motor (one non-fault state and 10 different fault states). For the multi-class classification problem, an evaluation of selected real-valued and complex-valued feedforward neural networks is considered. One finding is that in terms of accuracy, shallow MLMVN significantly outperform similarly constructed real-valued feedforward neural networks on the benchmark dataset. Thus, the high efficiency of such networks could be an advantage when processing data locally in order to improve robustness, performance, and reduce energy consumption on the system in use.

The way how we interact with banknotes is changing. This raises questions on how we interact with electronic payment systems. The general idea is to design low-cost electronics for cash handling systems. We establish a prototypical demonstrator which allows a consistent image capture quality and is able to handle complex algorithms for banknote authentication on cost-effective hardware. Therefore, tasks regarding reducing the evaluation time, without diminishing the reliability of the algorithms have to be considered. In this contribution we focus on the re-design of an authentication module for detection of commercial offset printing. This module analyses images in view to periodic printing patterns by means of the Discrete Fourier Transform (DFT). We propose to implement two concepts: an adaptive software architecture for DFT and parallel image processing. The re-design reduces evaluation time, without compromising the reliability of the authentication algorithm.