On Predictability of Revisioning in Corporate Cash Flow Forecasting

Financial services within corporations usually are part of an information system on which many business functions depend. As of the importance of forecast quality for financial services, means of forecast accuracy improvement, such as data-driven statistical prediction techniques and/or forecast support systems, have been subject to IS research since decades. In this paper we consider means of forecast improvement due to regular patterns in forecast revisioning. We analyze how business forecasts are adjusted to exploit possible improvements for the accuracy of forecasts with lower lead time. The empirical part bases on an unique dataset of experts\u27 cash flow forecasts and accountants\u27 actuals realizations of companies in a global corporation. We find that direction and magnitude of the final revision in aggregated forecasts can be related to suggested targets in earnings management, providing the means of improving the accuracy of longer-term cash flow forecasts

Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Road condition estimation based on Extended Floating Car Data (XFCD) from smart devices allows for determining given quality indicators like the international roughness index (IRI). Such approaches currently face the challenge to utilize measurements from heterogeneous sources. This paper investigates how a statistical learning based self-calibration overcomes individual sensor characteristics. We investigate how well the approach handles variations in the sensing frequency. Since the self-calibration approach requires the training of individual models for each participant, it is examined how a reduction of the amount of data sent to the backend system for training purposes affects the model performance. We show that reducing the amount of data by approximately 50 % does not reduce the models’ performance. Likewise, we observe that the approach can handle sensing frequencies up to 25 Hz without a performance reduction compared to the baseline scenario with 50 Hz

Weighted aggregation in the domain of crowd-based road condition monitoring

This paper focuses on crowd-based road condition monitoring using smart devices, such as smartphones and evaluates different strategies for aggregating multiple measurements (arithmetic mean and weighted means using R2 and RMSE) for predicting the longitudinal road roughness. The results confirm that aggregating predictions from single drives leads to a higher model performance. This has been expected and confirms the intuition. The overall R2 could be increased from 0.69 to 0.75 on average and the NRMSE could be decreased from 9% to 8% on average. However, contrary to the intuition, the results show that weighted aggregations of single predictions should be avoided, which is consistent with previous findings in other domains, such as financial forecasting

Road Condition Measurement and Assessment: A Crowd Based Sensing Approach

The widespread adoption of smart devices and vehicle sensors has the potential for an unprecedented real time assessment of road conditions. The international roughness index (IRI) is an important road profile quality indicator well suited for a crowd based sensing approach. One of the challenges, however, is the heterogeneous nature of sensor measurements from multiple cars that need to be integrated. In this paper, we propose a self-calibration approach that utilizes multiple statistical models trained individually for each car, which in turn get integrated into an overall view of the road segment’s IRI. We evaluate our approach on a dataset collected from seven drives with a total distance of 32 km, with a smartphone equipped car. The dataset contains GPS, accelerometer and gyroscope measurements. Our results show that this approach can reach a mean R² of 0.68 for single car predictions and a R² of 0.75 for combined predictions