Estimating European Temperature Trends

This paper presents estimates for common trends in European temperature panels using new estimators. The analyzed data contains 4000 Eurasian weather stations. A sampling algorithm robust against inherent geographical biases is developed, and appropriate estimators are evaluated. The estimations based on this evaluation show that commonalities in temperature movements disappear with growing geographical scope. They also reveal that European mean temperature increased by 1.8°C over the past 130 years, but estimates differ by region. A particularly pronounced increase has taken place since the 1980s. Further, a 20-year cycle is discovered, and a low-frequency fractal structure of temperature trends is proposed

Spatio-temporal Bayesian on-line changepoint detection with model selection

Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes. We propose spatially structured Vector Autoregressions (VARs) for modelling the process between changepoints (CPs) and give an upper bound on the approximation error of such models. The resulting algorithm performs prediction, model selection and CP detection on-line. Its time complexity is linear and its space complexity constant, and thus it is two orders of magnitudes faster than its closest competitor. In addition, it outperforms the state of the art for multivariate data.Comment: 10 pages, 7f figures, to appear in Proceedings of the 35th International Conference on Machine Learning 201

Doubly robust Bayesian inference for non-stationary streaming data with β-divergences

We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with β-divergences. The resulting inference procedure is doubly robust for both the predictive and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as β→0 . Secondly, we give a principled way of choosing the divergence parameter β by minimizing expected predictive loss on-line. We offer the state of the art and improve the False Discovery Rate of CP S by more than 80% on real world data