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There is a need for robust solutions to the challenges of spatio-temporal data quality assessment that include and go beyond assessment of accuracy. Emphasis is often placed on the quality assessment of individual observations from sensors but not on the sensors themselves nor upon site metadata such as location and timestamps. The focus of this paper is on the development and evaluation of such a representative, interpolation-based solution for the assessment of spatio-temporal data quality. We call our method the SMART method, short for Simple Mappings for the Approximation and Regression of Time series. A robust, linear mapping is determined between the observations from pairs of sites over a representative time period and a quadratic estimate of error is derived from these linear mappings. These mappings combine to form a robust interpolator that outperforms other popular interpolators in estimating ground truth in the presence of bad data, and that can be used to estimate ground truth and assess accuracy. The coefficients of the mappings and other derived measures can also help to identify problematic sites, including sites having incorrect location or timestamp metadata. When applied to a real-world, meteorological data set, we identify numerous problematic sites that otherwise have not been flagged as bad. We identify sites for which metadata is incorrect. We believe that there are many problems with real data sets like these and, in the absence of an approach like ours, these problems have largely gone unidentified. Our approach is novel for the simple but effective way that it accounts for spatial and temporal variation, and that it addresses more than just accuracy. [ABSTRACT FROM AUTHOR]

The authors generated synthetic multivariate time series data that are statistically similar to the real data and improved the flare prediction performance. With technological advancements, high-dimensional data, such as time series data, have been collected in much more abundance in recent years. Oregi et al. [[6]] present an active adaptation strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. [Extracted from the article]

Solar flare prediction is a central problem in space weather forecasting and has captivated the attention of a wide spectrum of researchers due to recent advances in both remote sensing as well as machine learning and deep learning approaches. The experimental findings based on both machine and deep learning models reveal significant performance improvements for task specific datasets. Along with building models, the practice of deploying such models to production environments under operational settings is a more complex and often time-consuming process which is often not addressed directly in research settings. We present a set of new heuristic approaches to train and deploy an operational solar flare prediction system for ≥ M1.0-class flares with two prediction modes: full-disk and active region-based. In full-disk mode, predictions are performed on full-disk line-of-sight magnetograms using deep learning models whereas in active region-based models, predictions are issued for each active region individually using multivariate time series data instances. The outputs from individual active region forecasts and full-disk predictors are combined to a final full- disk prediction result with a meta-model. We utilized an equal weighted average ensemble of two base learners' flare probabilities as our baseline meta learner and improved the capabilities of our two base learners by training a logistic regression model. The major findings of this study are: 1) We successfully coupled two heterogeneous flare prediction models trained with different datasets and model architecture to predict a full-disk flare probability for next 24 h, 2) Our proposed ensembling model, i.e., logistic regression, improves on the predictive performance of two base learners and the baseline meta learner measured in terms of two widely used metrics True Skill Statistic (TSS) and Heidke Skill Score (HSS), and 3) Our result analysis suggests that the logistic regression-based ensemble (Meta- FP) improves on the full-disk model (base learner) by ~9% in terms TSS and ~10% in terms of HSS. Similarly, it improves on the AR-based model (base learner) by ~17% and ~20% in terms of TSS and HSS respectively. Finally, when compared to the baseline meta model, it improves on TSS by ~10% and HSS by ~15%. [ABSTRACT FROM AUTHOR]

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining. [ABSTRACT FROM AUTHOR]

One of the major bottlenecks in refining supervised algorithms is data scarcity. This might be caused by a number of reasons often rooted in extremely expensive and lengthy data collection processes. In natural domains such as Heliophysics, it may take decades for sufficiently large samples for machine learning purposes. Inspired by the massive success of generative adversarial networks (GANs) in generating synthetic images, in this study we employed the conditional GAN (CGAN) on a recently released benchmark dataset tailored for solar flare forecasting. Our goal is to generate synthetic multivariate time-series data that (1) are statistically similar to the real data and (2) improve the performance of flare prediction when used to remedy the scarcity of strong flares. To evaluate the generated samples, first, we used the Kullback–Leibler divergence and adversarial accuracy measures to quantify the similarity between the real and synthetic data in terms of their descriptive statistics. Second, we evaluated the impact of the generated samples by training a predictive model on their descriptive statistics, which resulted in a significant improvement (over 1100% in TSS and 350% in HSS). Third, we used the generated time series to examine their high-dimensional contribution to mitigating the scarcity of the strong flares, which we also observed a significant improvement in terms of TSS (4%, 7%, and 31%) and HSS (75%, 35%, and 72%), compared to oversampling, undersampling, and synthetic oversampling methods, respectively. We believe our findings can open new doors toward more robust and accurate flare forecasting models. [ABSTRACT FROM AUTHOR]

Spatiotemporal event sequences (STESs) are the ordered series of event types whose instances frequently follow each other in time and are located close-by. An STES is a spatiotemporal frequent pattern type, which is discovered from moving region objects whose polygon-based locations continiously evolve over time. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. The quality of the discovered sequences is of great importance to the domain experts who use these algorithms. We introduce a novel algorithm to find the most relevant STESs without threshold values. We tested the relevance and performance of our threshold-free algorithm with a case study on solar event metadata, and compared the results with the previous STES mining algorithms. [ABSTRACT FROM AUTHOR]

With the widespread acceptance of RDF (Resource Description Framework) as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. Efficient querying of RDF data is therefore of increasing importance. Preference queries and fuzzy queries are required for personalized queries, and have been extensively investigated in the context of databases. Focusing on SPARQL (Simple Protocol and RDF Query Language), we propose a fuzzy extension of SPARQL with preferences in this paper. First, we extend SPARQL with fuzzy sets by allowing the occurrence of fuzzy terms, fuzzy operators and fuzzy relations. Second, we introduce multi-dimensional preferences into the fuzzy SPARQL. Based on fuzzy set theory, we also propose a set of translation rules to convert the fuzzy SPARQL with preferences into crisp SPARQL. Finally we propose an effective scoring function to calculate the order of query results, which integrates the preferences and membership degrees. The proposed approach is evaluated with experiments as well. [ABSTRACT FROM AUTHOR]