@inproceedings{61492, abstract = {{This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios. Various machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved. For the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}}, keywords = {{ML, Prediction, Tree Ensembles, GLOSA}}, location = {{Gold Coast (Australia)}}, publisher = {{IEEE}}, title = {{{ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}}}, volume = {{28}}, year = {{2026}}, } @unpublished{53793, abstract = {{We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance.}}, author = {{Harder, Hans and Peitz, Sebastian}}, keywords = {{extreme learning machines, partial differential equations, data-driven prediction, high-dimensional systems}}, title = {{{Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines}}}, year = {{2024}}, } @inproceedings{55336, abstract = {{Predicting the remaining useful life of technical systems has gained significant attention in recent years due to increasing demands for extending the lifespan of degrading system components. Therefore, already used systems are retrofitted by integrating sensors to monitor their performance and functionality, enabling accurate diagnosis of their condition and prediction of their remaining useful life. One of the main challenges in this field is identified in the missing data from the time where the retrofitted system has already run but without being monitored by sensors. In this paper, a novel approach for the combined diagnostics and prognostics of retrofitted systems is proposed. The methodology aims to provide an accurate diagnosis of the system’s health state and estimation of the remaining useful life by a combination of a machine learning and expert knowledge. To evaluate the effectiveness of the proposed methodology, a case study involving a retrofitted system in an industrial setting is selected and applied. It is demonstrated that the approach effectively diagnose the current system’s health state and accurately predict its remaining useful life, thereby enabling predictive maintenance and decision-making. Overall, our research contributes to advancing the field of condition monitoring for retrofitted systems by providing a comprehensive methodology that addresses the challenge of missing data.}}, author = {{Bender, Amelie and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}}, booktitle = {{Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)}}, isbn = {{979-8-3503-6058-5}}, keywords = {{retrofit, diagnosis, prognostics, RUL prediction, missing data, ball bearings}}, location = {{Stockholm, Schweden}}, publisher = {{IEEE Computer Society}}, title = {{{Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment}}}, doi = {{10.1109/PHM61473.2024.00038}}, year = {{2024}}, } @inproceedings{50479, abstract = {{Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.}}, author = {{Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga}}, booktitle = {{The Semantic Web – ISWC 2023}}, editor = {{R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}}, isbn = {{9783031472398}}, issn = {{0302-9743}}, keywords = {{temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs}}, location = {{Athens, Greece}}, pages = {{465–483}}, publisher = {{Springer, Cham}}, title = {{{TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs}}}, doi = {{10.1007/978-3-031-47240-4_25}}, volume = {{14265}}, year = {{2023}}, } @article{53801, abstract = {{In this study, we evaluate the impact of gender-biased data from German-language physician reviews on the fairness of fine-tuned language models. For two different downstream tasks, we use data reported to be gender biased and aggregate it with annotations. First, we propose a new approach to aspect-based sentiment analysis that allows identifying, extracting, and classifying implicit and explicit aspect phrases and their polarity within a single model. The second task we present is grade prediction, where we predict the overall grade of a review on the basis of the review text. For both tasks, we train numerous transformer models and evaluate their performance. The aggregation of sensitive attributes, such as a physician’s gender and migration background, with individual text reviews allows us to measure the performance of the models with respect to these sensitive groups. These group-wise performance measures act as extrinsic bias measures for our downstream tasks. In addition, we translate several gender-specific templates of the intrinsic bias metrics into the German language and evaluate our fine-tuned models. Based on this set of tasks, fine-tuned models, and intrinsic and extrinsic bias measures, we perform correlation analyses between intrinsic and extrinsic bias measures. In terms of sensitive groups and effect sizes, our bias measure results show different directions. Furthermore, correlations between measures of intrinsic and extrinsic bias can be observed in different directions. This leads us to conclude that gender-biased data does not inherently lead to biased models. Other variables, such as template dependency for intrinsic measures and label distribution in the data, must be taken into account as they strongly influence the metric results. Therefore, we suggest that metrics and templates should be chosen according to the given task and the biases to be assessed. }}, author = {{Kersting, Joschka and Maoro, Falk and Geierhos, Michaela}}, issn = {{0169-023X}}, journal = {{Data & Knowledge Engineering}}, keywords = {{Language model fairness, Aspect phrase classification, Grade prediction, Physician reviews}}, publisher = {{Elsevier}}, title = {{{Towards comparable ratings: Exploring bias in German physician reviews}}}, doi = {{10.1016/j.datak.2023.102235}}, volume = {{148}}, year = {{2023}}, } @phdthesis{41971, abstract = {{Ultraschall-Drahtbonden ist eine Standardtechnologie im Bereich der Aufbau- und Verbindungstechnik von Leistungshalbleitermodulen. Um Prozessschritte und damit wertvolle Zeit zu sparen, sollen die Kupferdickdrähte für die Leistungshalbleiter auch für die Kontaktierung von eingespritzten Anschlusssteckern im Modulrahmen verwendet werden. Das Kontaktierungsverfahren mit diesen Drähten auf Steckern in dünnwandigen Kunststoffrahmen führt häufig zu unzureichender Bondqualität. In dieser Arbeit wird das Bonden von Anschlusssteckern experimentell und anhand von Simulationen untersucht, um die Prozessstabilität zu steigern. Zunächst wurden Experimente auf Untergründen mit hoher Steifigkeit durchgeführt, um Störgrößen von Untergrundeigenschaften zu verringern. Die gewonnenen Erkenntnisse erlaubten die Entwicklung eines Simulationsmodells für die Vorhersage der Bondqualität. Dieses basiert auf einer flächenaufgelösten Reibarbeitsbestimmung im Fügebereich unter Berücksichtigung des Ultraschallerweichungseffektes und der hierdurch entstehenden hohen Drahtverformung. Experimente an den Anschlusssteckern im Modulrahmen zeigten eine verringerte Relativverschiebung zwischen Draht und Stecker, was zu einer deutlichen Verringerung der Reibarbeit führt. Außerdem wurden verminderte Schwingamplituden des Bondwerkzeugs nachgewiesen. Dies führt zu einer weiteren Reduktion der Reibarbeit. Beide Effekte wurden mithilfe eines Mehrmassenschwingers modelliert. Die gewonnenen Erkenntnisse und die erstellten Simulationsmodelle ermöglichen die Entwicklung von Klemmvorrichtungen, welche die identifizierten Störgrößen gezielt kompensieren und so ein verlässliches Bonden der Anschlussstecker im gleichen Prozessschritt ermöglichen, in dem auch die Leistungshalbleiter kontaktiert werden.}}, author = {{Althoff, Simon}}, isbn = {{978-3-8440-8903-5}}, keywords = {{heavy copper bonding, wire bonding, quality prediction, friction model, point-contact-element}}, pages = {{192}}, publisher = {{Shaker}}, title = {{{Predicting the Bond Quality of Heavy Copper Wire Bonds using a Friction Model Approach}}}, volume = {{15}}, year = {{2023}}, } @inproceedings{22724, abstract = {{ Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach. In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties. Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results.}}, author = {{Bender, Amelie and Sextro, Walter}}, booktitle = {{Proceedings of the European Conference of the PHM Society 2021}}, editor = {{Do, Phuc and King, Steve and Fink, Olga}}, keywords = {{Hybrid prediction method, Multi-model particle filtering, Uncertainty quantification, RUL estimation}}, number = {{1}}, title = {{{Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties}}}, doi = {{https://doi.org/10.36001/phme.2021.v6i1.2843 }}, volume = {{6}}, year = {{2021}}, } @inbook{21542, abstract = {{Using near-field (NF) scan data to predict the far-field (FF) behaviour of radiating electronic systems represents a novel method to accompany the whole RF design process. This approach involves so-called Huygens' box as an efficient radiation model inside an electromagnetic (EM) simulation tool and then transforms the scanned NF measured data into the FF. For this, the basic idea of the Huygens'box principle and the NF-to-FF transformation are briefly presented. The NF is measured on the Huygens' box around a device under test using anNF scanner, recording the magnitude and phase of the site-related magnetic and electric components. A comparison between a fullwave simulation and the measurement results shows a good similarity in both the NF and the simulated and transformed FF.Thus, this method is applicable to predict the FF behaviour of any electronic system by measuring the NF. With this knowledge, the RF design can be improved due to allowing a significant reduction of EM compatibility failure at the end of the development flow. In addition, the very efficient FF radiation model can be used for detailed investigations in various environments and the impact of such an equivalent radiation source on other electronic systems can be assessed.}}, author = {{Schröder, Dominik and Lange, Sven and Hangmann, Christian and Hedayat, Christian}}, booktitle = {{Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis}}, isbn = {{9781839530494}}, keywords = {{Huygens' box, NF-to-FF transformation, efficient FF radiation model, FF behaviour, EMI assessment, PCB, near-field measurements, efficient radiation model, far-field behaviour, RF design process, far-field prediction, Huygens'box principle, fullwave simulation, electronic system radiation, equivalent radiation source, electromagnetic simulation tool, near-field scan data, EM compatibility failure reduction}}, pages = {{315--346 (32)}}, publisher = {{ The Institution of Engineering and Technology (IET)}}, title = {{{Far-field prediction combining simulations with near-field measurements for EMI assessment of PCBs}}}, doi = {{10.1049/pbcs072e_ch14}}, year = {{2020}}, } @inproceedings{17810, abstract = {{In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.}}, author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}}, booktitle = {{PHM Society European Conference}}, keywords = {{PHM 2019, crack propagation, forecasting, unevenly spaced time series, step ahead prediction, short time series}}, number = {{1}}, title = {{{Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}}}, volume = {{5}}, year = {{2020}}, } @inproceedings{13460, abstract = {{Remaining useful lifetime (RUL) predictions as part of a condition monitoring system are focused in more and more research and industrial applications. To establish an efficient and precise estimate of the RUL of a technical product, different uncertainties have to be handled. To minimize the uncertainties of the RUL estimation, a reliable and accurate prognostic approach as well as a good failure threshold are important. Regarding the failure threshold, most often an expert sets a fixed failure threshold. However, neither the a priori known failure threshold nor a fixedthreshold value are feasible in every application. Especially in the case of varying characteristics of the monitored system, an adaptive failure threshold is of great importance concerning the accuracy of the RUL estimation. Rubber-metal-elements, which are used in a wide range of applications for vibration and sound isolation, are mon-itored by thermocouples to allow for lifetime predictions. Therefore, the element’s state is described by its temper-ature during its service life. Aiming to establish accurate RUL predictions of a rubber-metal-element, uncertainties due to nonlinear material characteristics and changing operational conditions have to be considered. Consequently, different temperature-based failure threshold definitions are implemented and compared within a particle filtering approach. }}, author = {{Bender, Amelie and Schinke, Lennart and Sextro, Walter}}, booktitle = {{Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)}}, editor = {{Beer, Michael and Zio, Enrico}}, isbn = {{978-981-11-2724-3}}, keywords = {{RUL prediction, adaptive threshold, prognostics, condition monitoring}}, location = {{Hannover}}, number = {{29}}, pages = {{1262--1269}}, title = {{{Remaining useful lifetime prediction based on adaptive failure thresholds}}}, year = {{2019}}, } @techreport{20868, abstract = {{This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on stochastic processes and incorporates firm-level and industry-sector developments. A large-scale empirical implementation for US-listed firms over the period of 1980-2010 shows important improvements in the discriminatory accuracy and demonstrates incremental information content beyond state-of-the-art accounting and market-based prediction models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners.}}, author = {{Sievers, Sönke and Klobucnik, Jan and Miersch, David}}, keywords = {{Financial distress prediction, probability of default, accounting information, stochastic processes, simulation}}, pages = {{84}}, title = {{{Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}}}, doi = {{10.2139/ssrn.2237757}}, year = {{2017}}, } @article{5199, abstract = {{This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on stochastic processes and incorporates firm-level and industry-sector developments. A large-scale empirical implementation for US-listed firms over the period of 1980-2010 shows important improvements in the discriminatory accuracy and demonstrates incremental information content beyond state-of-the-art accounting and market-based prediction models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners. }}, author = {{Klobucnik, Jan and Miersch, David and Sievers, Sönke}}, journal = {{SSRN Electronic Journal}}, keywords = {{Financial distress prediction, probability of default, accounting information, stochastic processes, simulation}}, title = {{{Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}}}, year = {{2017}}, } @inproceedings{9889, abstract = {{A measurement method is presented that combines the advantages of the multisine measurement technique with a prediction method for peak bending behavior. This combination allows the analysis of the dynamic behavior of mechanical structures at distinctly reduced measurement durations and has the advantage of reducing high excitation impacts on the structure under test.}}, author = {{Sprock, Christian and Sextro, Walter}}, booktitle = {{Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International}}, keywords = {{bending, dynamic testing, measurement, structural engineering, vibrations, measurement durations, mechanical structures, multisine measurement technique, nonlinear peak bending behavior, prediction method, time-efficient dynamic analysis, Heuristic algorithms, Nonlinear systems, Oscillators, Time measurement, Time-frequency analysis, Vibrations}}, pages = {{320--324}}, title = {{{Time-efficient dynamic analysis of structures exhibiting nonlinear peak bending}}}, doi = {{10.1109/I2MTC.2014.6860760}}, year = {{2014}}, } @inproceedings{46388, abstract = {{Understanding the behaviour of well-known algorithms for classical NP-hard optimisation problems is still a difficult task. With this paper, we contribute to this research direction and carry out a feature based comparison of local search and the well-known Christofides approximation algorithm for the Traveling Salesperson Problem. We use an evolutionary algorithm approach to construct easy and hard instances for the Christofides algorithm, where we measure hardness in terms of approximation ratio. Our results point out important features and lead to hard and easy instances for this famous algorithm. Furthermore, our cross-comparison gives new insights on the complementary benefits of the different approaches.}}, author = {{Nallaperuma, Samadhi and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike}}, booktitle = {{Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII}}, isbn = {{9781450319904}}, keywords = {{approximation algorithms, local search, traveling salesperson problem, feature selection, prediction, classification}}, pages = {{147–160}}, publisher = {{Association for Computing Machinery}}, title = {{{A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem}}}, doi = {{10.1145/2460239.2460253}}, year = {{2013}}, } @techreport{20870, abstract = {{This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions.}}, author = {{Sievers, Sönke and Mokwa, Christopher Frederik}}, keywords = {{Management forecast biases, cross-sectional projection models, venture-backed start-ups, failure prediction, overoptimism, overconfidence}}, pages = {{31}}, title = {{{The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments}}}, doi = {{10.2139/ssrn.2100501}}, year = {{2012}}, } @inproceedings{9784, abstract = {{Piezoelectric inertia motors use the inertia of a body to drive it by means of a friction contact in a series of small steps. These motors can operate in ``stick-slip'' or ``slip-slip'' mode, with the fundamental frequency of the driving signal ranging from several Hertz to more than 100 kHz. To predict the motor characteristics, a Coulomb friction model is sufficient in many cases, but numerical simulation requires microscopic time steps. This contribution proposes a much faster simulation technique using one evaluation per period of the excitation signal. The proposed technique produces results very close to those of timestep simulation for ultrasonics inertia motors and allows direct determination of the steady-state velocity of an inertia motor from the motion profile of the driving part. Thus it is a useful simulation technique which can be applied in both analysis and design of inertia motors, especially for parameter studies and optimisation.}}, author = {{Hunstig, Matthias and Hemsel, Tobias and Sextro, Walter}}, booktitle = {{Ultrasonics Symposium (IUS), 2012 IEEE International}}, issn = {{1948-5719}}, keywords = {{friction, ultrasonic motors, Coulomb friction model, efficient simulation technique, friction contact, high-frequency piezoelectric inertia motor, motor characteristics prediction, numerical simulation, slip-slip mode, stick-slip mode, time-step simulation, ultrasonic inertia motor, Acceleration, Acoustics, Actuators, Computational modeling, Friction, Numerical models, Steady-state}}, pages = {{277--280}}, title = {{{An efficient simulation technique for high-frequency piezoelectric inertia motors}}}, doi = {{10.1109/ULTSYM.2012.0068}}, year = {{2012}}, } @article{5196, abstract = {{This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions. }}, author = {{Mokwa, Christopher Frederik and Sievers, Sönke}}, journal = {{SSRN Electronic Journal}}, keywords = {{Management forecast biases, cross-sectional projection models, venture-backed start-ups, failure prediction, overoptimism, overconfidence}}, title = {{{The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments}}}, doi = {{10.2139/ssrn.2100501}}, year = {{2012}}, }