Probabilistic Anomaly Detection in Natural Gas Time Series Data

This paper introduces a probabilistic approach to anomaly detection, specifically in natural gas time series data. In the natural gas field, there are various types of anomalies, each of which is induced by a range of causes and sources. The causes of a set of anomalies are examined and categorized, and a Bayesian maximum likelihood classifier learns the temporal structures of known anomalies. Given previously unseen time series data, the system detects anomalies using a linear regression model with weather inputs, after which the anomalies are tested for false positives and classified using a Bayesian classifier. The method can also identify anomalies of an unknown origin. Thus, the likelihood of a data point being anomalous is given for anomalies of both known and unknown origins. This probabilistic anomaly detection method is tested on a reported natural gas consumption data set

The applicability of the T/S method to geopotential anomaly computations in the Northeast Atlantic

Methods are tested for computing geopotential anomalies from temperature data in the subtropical Northeast Atlantic. Mean temperature-salinity, salinity-depth and density-depth relationships are determined for 3 x 3° squares, using hydrographie data from World Oceanographie Data Centre A. Geopotential anomalies computed from observed temperatures and salinities from these mean relationships are compared with anomalies from the original temperature and salinity data. For 0-500 dbar, geopotential anomalies can be weil approximated, and the methods also work reasonably weil for 0-1000 dbar. The approximation is poor for 0-2000 dbar. Appropriate methods for obtaining the best results in each 3 x 3° square are specified. The method is applied to a particular subset of the data

Deep learning for inferring cause of data anomalies

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.Comment: Presented at ACAT 2017 conference, Seattle, US

On the anomalies in the latest LHCb data

Depending on the assumptions on the power corrections to the exclusive b -> s l+ l- decays, the latest data of the LHCb collaboration - based on the 3 fb^-1 data set and on two different experimental analysis methods - still shows some tensions with the SM predictions. We present a detailed analysis of the theoretical inputs and various global fits to all the available b -> s l+ l- data. This constitutes the first global analysis of the new data of the LHCb collaboration based on the hypothesis that these tensions can be at least partially explained by new physics contributions. In our model-independent analysis we present one-, two-, four-, and also five-dimensional global fits in the space of Wilson coefficients to all available b -> s l+ l- data. We also compare the two different experimental LHCb analyses of the angular observables in B -> K* mu+ mu-. We explicitly analyse the dependence of our results on the assumptions about power corrections, but also on the errors present in the form factor calculations. Moreover, based on our new global fits we present predictions for ratios of observables which may show a sign of lepton non-universality. Their measurements would crosscheck the LHCb result on the ratio R_K = BR(B+ -> K+ mu+ mu-) / BR(B+ -> K+ e+ e-) in the low-q^2 region which deviates from the SM prediction by 2.6 sigma.Comment: 41 pages, 24 figures. v2: references and comment on 1006.4945 [hep-ph] adde

Alignment and signed-intensity anomalies in WMAP data

Significant alignment and signed-intensity anomalies of local features of the cosmic microwave background (CMB) are detected on the three-year WMAP data, through a decomposition of the signal with steerable wavelets on the sphere. Firstly, an alignment analysis identifies two mean preferred planes in the sky, both with normal axes close to the CMB dipole axis. The first plane is defined by the directions toward which local CMB features are anomalously aligned. A mean preferred axis is also identified in this plane, located very close to the ecliptic poles axis. The second plane is defined by the directions anomalously avoided by local CMB features. This alignment anomaly provides further insight on recent results (Wiaux et al. 2006). Secondly, a signed-intensity analysis identifies three mean preferred directions in the southern galactic hemisphere with anomalously high or low temperature of local CMB features: a cold spot essentially identified with a known cold spot (Vielva et al. 2004), a second cold spot lying very close to the southern end of the CMB dipole axis, and a hot spot lying close to the southern end of the ecliptic poles axis. In both analyses, the anomalies are observed at wavelet scales corresponding to angular sizes around 10 degress on the celestial sphere, with global significance levels around 1%. Further investigation reveals that the alignment and signed-intensity anomalies are only very partially related. Instrumental noise, foreground emissions, as well as some form of other systematics, are strongly rejected as possible origins of the detections. An explanation might still be envisaged in terms of a global violation of the isotropy of the Universe, inducing an intrinsic statistical anisotropy of the CMB.Comment: 12 pages, 7 figures. Accepted for publication in MNRAS. Small changes made (including the new subsection 3.4) to match the final versio

Data Imputation through the Identification of Local Anomalies

We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose i) a novel algorithm to efficiently separate, i.e., detect and localize, possible corruptions from a given suspicious data instance and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As a generalization to Euclidean distance, we also propose a novel distance measure, which is based on the ranked deviations among the data attributes and empirically shown to be superior in separating the corruptions. Our algorithm first splits the suspicious instance into parts through a binary partitioning tree in the space of data attributes and iteratively tests those parts to detect local anomalies using the nominal statistics extracted from an uncorrupted (clean) reference data set. Once each part is labeled as anomalous vs normal, the corresponding binary patterns over this tree that characterize corruptions are identified and the affected attributes are imputed. Under a certain conditional independency structure assumed for the binary patterns, we analytically show that the false alarm rate of the introduced algorithm in detecting the corruptions is independent of the data and can be directly set without any parameter tuning. The proposed framework is tested over several well-known machine learning data sets with synthetically generated corruptions; and experimentally shown to produce remarkable improvements in terms of classification purposes with strong corruption separation capabilities. Our experiments also indicate that the proposed algorithms outperform the typical approaches and are robust to varying training phase conditions

On estimating gravity anomalies from gradiometer data

The Gravsat-gradiometer mission involves flying a gradiometer on a gravity satellite (Gravsat) which is in a low, polar, and circular orbit. Results are presented of a numerical simulation of the mission which demonstrates that, if the satellite is in a 250-km orbit, 3- and 5-degree gravity anomalies may be estimated with accuracies of 0.03 and 0.01 mm/square second (3 and 1 mgal), respectively. At an altitude of 350 km, the results are 0.07 and 0.025 mm.square second (7 and 2.5 mgal), respectively. These results assume a rotating type gradiometer with a 0.1 -etvos unit accuracy. The results can readily be scaled to reflect another accuracy level