Maximally Divergent Intervals for Anomaly Detection

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals.Comment: ICML Workshop on Anomaly Detectio

Spatiotemporal model for benchmarking causal discovery algorithms

We propose a spatiotemporal model system to evaluate methods of causal discovery. The use of causal discovery to improve our understanding of the spatiotemporal complex system Earth has become widespread in recent years (Runge et al., Nature Comm. 2019). A widespread application example are the complex teleconnections among major climate modes of variability. The challenges in estimating such causal teleconnection networks are given by (1) the requirement to reconstruct the climate modes from gridded climate fields (dimensionality reduction) and (2) by general challenges for causal discovery, for instance, high dimensionality and nonlinearity. Both challenges are currently being tackled independently. Both dimensionality reduction methods and causal discovery have made strong progress in recent years, but the interaction between the two has not yet been much tackled so far. Thanks to projects like CMIP a vast amount of climate data is available. In climate models climate modes of variability emerge as macroscale features and it is challenging to objectively benchmark both dimension reduction and causal discovery methods since there is no ground truth for such emergent properties. We propose a spatiotemporal model system that encodes causal relationships among well-defined modes of variability. The model can be thought of as an extension of vector-autoregressive models well-known in time series analysis. This model provides a framework for experimenting with causal discovery in large spatiotemporal models. For example, researchers can analyze how the performance of an algorithm is affected under different methods of dimensionality reduction and algorithms for causal discovery. Also challenging features such as non-stationarity and regime-dependence can be modelled and evaluated. Such a model will help the scientific community to improve methods of causal discovery for climate science. Runge, J., S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, M. Kretschmer, M. D. Mahecha, J. Muñoz-Marı́, E. H. van Nes, J. Peters, R. Quax, M. Reichstein, M. Scheffer, B.Schölkopf, P. Spirtes, G. Sugihara, J. Sun, K. Zhang, and J. Zscheischler (2019). Inferring causation from time series in earth system sciences. Nature Communications 10 (1), 2553

EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery at 20 m resolution, matching topography and mesoscale (1.28 km) meteorological variables packaged into 32000 samples. Additionally we frame EarthNet2021 as a challenge allowing for model intercomparison. Resulting forecasts will greatly improve (> ×50) over the spatial resolution found in numerical models. This allows localized impacts from extreme weather to be redicted, thus supporting downstream applications such as crop yield prediction, forest health assessments or biodiversity monitoring. Find data, code, and how to participate at www.earthnet.tech

A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model

Describing ecosystem carbon fluxes is essential for deepening the understanding of the Earth system. However, partitioning net ecosystem exchange (NEE), i.e. the sum of ecosystem respiration (R eco ) and gross primary production (GPP), into these summands is ill-posed since there can be infinitely many mathematically-valid solutions. We propose a novel data-driven approach to NEE partitioning using a deep state space model which combines the interpretability and uncertainty analysis of state space models with the ability of recurrent neural networks to learn the complex functions governing the data. We validate our proposed approach on the FLUXNET dataset. We suggest using both the past and the future of R eco ’s predictors for training along with the nighttime NEE (NEE night ) to learn a dynamical model of R eco . We evaluate our nighttime R eco forecasts by comparing them to the ground truth NEE night and obtain the best accuracy with respect to other partitioning methods. The learned nighttime R eco model is then used to forecast the daytime R eco conditioning on the future observations of different predictors, i.e., global radiation, air temperature, precipitation, vapor pressure deficit, and daytime NEE (NEE day ). Subtracted from the NEE day , these estimates yield the GPP, finalizing the partitioning. Our purely data-driven daytime R eco forecasts are in line with the recent empirical partitioning studies reporting lower daytime R eco than the Reichstein method, which can be attributed to the Kok effect, i.e., the plant respiration being higher at night. We conclude that our approach is a good alternative for data-driven NEE partitioning and complements other partitioning methods