SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

Díscola, Sérgio Luisir; Cecatto, J. R.; Fernandes, Marcio Merino; Ribeiro, Marcela Xavier

doi:10.3390/info9010008

Cited by 3 publications

(2 citation statements)

References 29 publications

(39 reference statements)

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“…The solar radiation incidence attribute values (r_inc) come from the panels mounted on each monitoring station [44,45] and will be exploited for this experiment.…”

Section: Task 1-forecasting Future Data (Istat Dataset)mentioning

confidence: 99%

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

2018

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This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

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“…The solar radiation incidence attribute values (r_inc) come from the panels mounted on each monitoring station [44,45] and will be exploited for this experiment.…”

Section: Task 1-forecasting Future Data (Istat Dataset)mentioning

confidence: 99%

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

2018

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“…A Smart AgriFood theoretical design is planned in Kaloxylos et al [22], though the creators of [23] present web submission in the agri-nourishment area; Poppe in [24] recommend the investigation to together the extension furthermore the association of ranch generation controls. Garba [25] creates shrewd water-distribution strategies in semi-bone-dry districts; Hlaing et al Present place infections acknowledgment utilizing measurable models; and, in addition, in Alipio et al, there are brilliant hydroponics frameworks with the intention of misuse derivation in Bayesian systems.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications on Agricultural

2019

IJITEE

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The focal point of this investigation is the structure and advancement of viable undertakings, running from yield collect and estimating to absent or wrong sensors information remaking, abusing and contrasting different machine learning procedures with recommend toward which heading to use endeavors and speculations. To deal with a blended data and information originating from genuine datasets that gather a sensor and physical qualities. As beneficial organizations, open or private, huge or little need expanding productivity with costs decrease, finding suitable approaches to misuse information that are ceaselessly recorded and influenced accessible to can be the correct decision to accomplish these objectives. Agrarian field is just clearly obstinate to the advanced innovation and the "shrewd homestead" demonstrate is progressively across the board by misusing the Internet of Things (IoT) worldview connected toward ecological moreover verifiable data from side to side timearrangement. The outcomes demonstrate how there are sufficient edges for development while supporting solicitations and necessities originating from organizations that desire to utilize a practical and enhanced horticulture mechanical business, putting in innovation, as well as in the information also in talented labor force compulsory to remove the greatest from it.

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Handling Imbalanced Time Series Through Ensemble of Classifiers: A Multi-class Approach for Solar Flare Forecasting

Díscola

Cecatto

Fernandes

et al. 2019

16th International Conference on Information Technology-New Generations (ITNG 2019)

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Solar Flares (SF) are sudden releases of large amounts of energy from the solar atmosphere [1]. They are categorized into 5 classes, namely, A, B, C, M and X, respectively in order of their strength, where SF of class A are the least harmful, while X flares are the most powerful and dangerous ones. They are categorized according to the level of X-ray emitted by Sun during the event. These phenomena impact satellite communications [2], Global Positioning System (GPS) and may also produce electricity power blackouts. So, it is imperative to develop robust solar flare forecasting systems. The features that influence the forecasting process are not known. We can find papers that use features derived from magnetogram vector [3-5], sunspot area [6], radio flux or Xray flux [7] and [8] the X-rays time series. Solar Flare datasets are extremely imbalanced. Most work in literature using traditional classifiers that deal with imbalanced datasets have the drawback of producing biased results [9-11]. An alternative to handle the poor results of the learning in imbalanced data is the usage of an Ensemble of Classifiers (EC) [12-14]. The EC main goal is to improve "weak" classification methods by applying many of weak classifiers (also called base inducers), so that the final classification may produce more accurate results. In this sense,

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SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

Cited by 3 publications

References 29 publications

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machine Learning Applications on Agricultural

Handling Imbalanced Time Series Through Ensemble of Classifiers: A Multi-class Approach for Solar Flare Forecasting

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