TY - JOUR A1 - Thon, Ingo A1 - Landwehr, Niels A1 - De Raedt, Luc T1 - Stochastic relational processes efficient inference and applications JF - Machine learning N2 - One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations. KW - Statistical relational learning KW - Stochastic relational process KW - Markov processes KW - Time series KW - CP-Logic Y1 - 2011 U6 - https://doi.org/10.1007/s10994-010-5213-8 SN - 0885-6125 VL - 82 IS - 2 SP - 239 EP - 272 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Gairing, Jan A1 - Högele, Michael A1 - Kosenkova, Tetiana T1 - Transportation distances and noise sensitivity of multiplicative Levy SDE with applications JF - Stochastic processes and their application N2 - This article assesses the distance between the laws of stochastic differential equations with multiplicative Levy noise on path space in terms of their characteristics. The notion of transportation distance on the set of Levy kernels introduced by Kosenkova and Kulik yields a natural and statistically tractable upper bound on the noise sensitivity. This extends recent results for the additive case in terms of coupling distances to the multiplicative case. The strength of this notion is shown in a statistical implementation for simulations and the example of a benchmark time series in paleoclimate. KW - Stochastic differential equations KW - Multiplicative Levy noise KW - Levy type processes KW - Heavy-tailed distributions KW - Model selection KW - Wasserstein distance KW - Time series Y1 - 2017 U6 - https://doi.org/10.1016/j.spa.2017.09.003 SN - 0304-4149 SN - 1879-209X VL - 128 IS - 7 SP - 2153 EP - 2178 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Sysoev, Ilya V. A1 - Ponomarenko, Vladimir I. A1 - Pikovskij, Arkadij T1 - Reconstruction of coupling architecture of neural field networks from vector time series JF - Communications in nonlinear science & numerical simulation N2 - We propose a method of reconstruction of the network coupling matrix for a basic voltage-model of the neural field dynamics. Assuming that the multivariate time series of observations from all nodes are available, we describe a technique to find coupling constants which is unbiased in the limit of long observations. Furthermore, the method is generalized for reconstruction of networks with time-delayed coupling, including the reconstruction of unknown time delays. The approach is compared with other recently proposed techniques. KW - Network reconstruction KW - Time series KW - Neurooscillators KW - Time delay Y1 - 2017 U6 - https://doi.org/10.1016/j.cnsns.2017.10.006 SN - 1007-5704 SN - 1878-7274 VL - 57 SP - 342 EP - 351 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bürger, Gerd A1 - Pfister, A. A1 - Bronstert, Axel T1 - Temperature-Driven Rise in Extreme Sub-Hourly Rainfall JF - Journal of climate N2 - Estimates of present and future extreme sub-hourly rainfall are derived from a daily spatial followed by a sub-daily temporal downscaling, the latter of which incorporates a novel, and crucial, temperature sensitivity. Specifically, daily global climate fields are spatially downscaled to local temperature T and precipitation P, which are then disaggregated to a temporal resolution of 10 min using a multiplicative random cascade model. The scheme is calibrated and validated with a group of 21 station records of 10-min resolution in Germany. The cascade model is used in the classical (denoted as MC) and in the new T-sensitive (MC+) version, which respects local Clausius-Clapeyron (CC) effects such as CC scaling. Extreme P is positively biased in both MC versions. Observed T sensitivity is absent in MC but well reproduced by MC+. Long-term positive trends in extreme sub-hourly P are generally more pronounced and more significant in MC+ than in MC. In units of 10-min rainfall, observed centennial trends in annual exceedance counts (EC) of P > 5 mm are +29% and in 3-yr return levels (RL) +27%. For the RCP4.5-simulated future, higher extremes are projected in both versions MC and MC+: per century, EC increases by 30% for MC and by 83% for MC+; the RL rises by 14% for MC and by 33% for MC+. Because the projected daily P trends are negligible, the sub-daily signal is mainly driven by local temperature. KW - Extreme events KW - Rainfall KW - Climate change KW - Statistical techniques KW - Time series KW - Stochastic models Y1 - 2019 U6 - https://doi.org/10.1175/JCLI-D-19-0136.1 SN - 0894-8755 SN - 1520-0442 VL - 32 IS - 22 SP - 7597 EP - 7609 PB - American Meteorological Soc. CY - Boston ER - TY - JOUR A1 - Schneider, Johannes A1 - Wenig, Phillip A1 - Papenbrock, Thorsten T1 - Distributed detection of sequential anomalies in univariate time series JF - The VLDB journal : the international journal on very large data bases N2 - The automated detection of sequential anomalies in time series is an essential task for many applications, such as the monitoring of technical systems, fraud detection in high-frequency trading, or the early detection of disease symptoms. All these applications require the detection to find all sequential anomalies possibly fast on potentially very large time series. In other words, the detection needs to be effective, efficient and scalable w.r.t. the input size. Series2Graph is an effective solution based on graph embeddings that are robust against re-occurring anomalies and can discover sequential anomalies of arbitrary length and works without training data. Yet, Series2Graph is no t scalable due to its single-threaded approach; it cannot, in particular, process arbitrarily large sequences due to the memory constraints of a single machine. In this paper, we propose our distributed anomaly detection system, short DADS, which is an efficient and scalable adaptation of Series2Graph. Based on the actor programming model, DADS distributes the input time sequence, intermediate state and the computation to all processors of a cluster in a way that minimizes communication costs and synchronization barriers. Our evaluation shows that DADS is orders of magnitude faster than S2G, scales almost linearly with the number of processors in the cluster and can process much larger input sequences due to its scale-out property. KW - Distributed programming KW - Sequential anomaly KW - Actor model KW - Data mining KW - Time series Y1 - 2021 U6 - https://doi.org/10.1007/s00778-021-00657-6 SN - 1066-8888 SN - 0949-877X VL - 30 IS - 4 SP - 579 EP - 602 PB - Springer CY - Berlin ER - TY - JOUR A1 - Pohle, Jennifer A1 - Adam, Timo A1 - Beumer, Larissa T1 - Flexible estimation of the state dwell-time distribution in hidden semi-Markov models JF - Computational statistics & data analysis N2 - Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN. KW - Penalized likelihood KW - Smoothing KW - Time series KW - Animal movement modeling Y1 - 2022 U6 - https://doi.org/10.1016/j.csda.2022.107479 SN - 0167-9473 SN - 1872-7352 VL - 172 PB - Elsevier CY - Amsterdam ER -