Mathieu Sinn

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Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. In this paper, we describe an efficient non-parametric algorithm which provides highly accurate predictions based on real-time GPS measurements. The key idea is to use a Kernel Regression model to represent the dependencies between(More)
This paper explores kernel spectral clustering methods to improve forecasts of aggregated electricity smart meter data. The objective is to cluster the data in such a way that building a forecasting models separately for each cluster and taking the sum of forecasts leads to a better accuracy than building one forecasting model for the total aggregate of all(More)
Two-photon microscopy has substantially advanced our understanding of cellular dynamics in the immune system. Cell migration can now be imaged in real time in the living animal. Strikingly, the migration of naive lymphocytes in secondary lymphoid tissue appears predominantly random. It is unclear, however, whether directed migration may escape detection in(More)
Ordinal time series analysis is a new approach to the investigation of long and complex time series, which bases on ordinal patterns describing the order relations between the values of a time series. In this paper we consider ordinal time series analysis from the conceptional viewpoint. In particular, we introduce ordinal processes as models for ordinal(More)
For a zero-mean Gaussian process, the covariances of zero crossings can be expressed as the sum of quadrivariate normal orthant probabilities. In this paper, we demonstrate the evaluation of zero crossing covariances using one-dimensional integrals. Furthermore, we provide asymptotics of zero crossing covariances for large time lags and derive bounds and(More)
Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and leptokurtic caused by the nature of energy data. In this paper we propose a novel approach to estimate the(More)
In order to develop fast and robust methods for extracting qualitative information from non-linear time series, Bandt and Pompe have proposed to consider time series from the pure ordinal viewpoint. On the base of counting ordinal patterns, which describe the up-and-down in a time series, they have introduced the concept of permutation entropy for(More)
Building efficient and sustainable transportation systems is a key challenge for accommodating the fast-increasing population living in cities. Lack of efficiency in transportation networks typically arises from uncertainty, e.g., about the availability of resources (such as parking lots or bicycles in bike sharing systems), or the exogenous factors(More)
As a new method for detecting change-points in high-resolution time series, we apply Maximum Mean Discrepancy to the distributions of ordinal patterns in different parts of a time series. The main advantage of this approach is its computational simplicity and robustness with respect to (non-linear) monotonic transformations, which makes it particularly(More)
The distribution of ordinal patterns in time series has been found to reflect important qualitative features of the underlying system dynamics. Abrupt changes in the dynamics typically result in clearly visible differences between the distributions before and after the break. Recurring dynamical regimes can be discovered by classifying the distributions in(More)