Comparison of different predictive models and their effectiveness in sunspot number prediction

Moustafa, Sayed S. R.; Khodairy, S.

doi:10.1088/1402-4896/acc21a

Cited by 14 publications

(14 citation statements)

References 44 publications

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“…They provide insights into the solar cycle, an eleven-year period of fluctuating solar activity, and help understand solar magnetism and space weather phenomena. Sunspot analysis also enhancing our knowledge of stellar processes [1,2].…”

Section: Introductionmentioning

confidence: 94%

Evaluating the concordance of Egyptian and international sunspot observations

Moustafa,

Khodairy

2024

Phys. Scr.

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This investigation presents a comprehensive analysis of the historical trajectory of sunspot number (SSN) observations in Egypt, a country renowned for its rich astronomical heritage. Despite Egypt's long-standing practice in solar observation, the local SSN datasets are marred by a significant incidence of missing entries, posing formidable obstacles to the accurate evaluation of solar activity. Addressing this challenge, the study employs dynamic time warping (DTW) as a methodological tool to assess the alignment of local and global SSN datasets. This technique adeptly harmonizes these datasets by reconciling temporal inconsistencies and variations in sampling rates. Subsequent to the application of DTW, the research integrates orthogonal regression for the imputation of the absent values in the Egyptian SSN dataset. This method, preferred for its proficiency in managing errors in both the dependent and independent variables, deviates from conventional linear regression techniques, thereby providing a more nuanced approach to data approximation. The analysis unveils a substantial correlation between the estimated local SSN values and the global SSN indices, with the former consistently exhibiting lower figures. Nevertheless, these local values display parallel trends and seasonal fluctuations akin to those observed in the global dataset, validating the imputation method and highlighting the unique characteristics of the Egyptian SSN data within the global context of solar activity monitoring. The implications of these findings are significant for the discipline of solar physics, especially for regions contending with incomplete datasets. The methodologies advanced in this research offer a robust framework for the enhancement of datasets with missing data, thus broadening the comprehension of solar phenomena.

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Section: Introductionmentioning

confidence: 94%

Evaluating the concordance of Egyptian and international sunspot observations

Moustafa,

Khodairy

2024

Phys. Scr.

Self Cite

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“…we talked about evaluating classi cation models, we started by counting the number of prediction errors that the model makes [15]. This is not appropriate for a regression problem.…”

Section: Error Measuresmentioning

confidence: 99%

Fire prediction using Machine Learning Algorithms based on the confusion matrix

Korchi

Abatal

Essaid

2023

Preprint

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In an earlier article, we outlined the process of developing a Machine Learning project that is often complex to establish, and that the problem must be broken down into several stages to facilitate its resolution [1]. We were able to identify 5 steps that we think are the most important to tackle such a project. These 5 steps are : the definition of the problem, the preparation of the data, the choice of the right algorithms, the optimization of the results and the presentation of the final results. In this manuscript, we will propose the application of different ways of evaluating classification models through an algorithm that predicts whether there is a fire in a given location or not. We are conscious that this problem is difficult to solve, especially when we have to predict the latter when there is none, do not predict when there is really one, or not predict when there is none. The method of this study will show how to choose the right algorithm and how to Evaluate it. The experiment shows promising results obtained thanks to the classification model algorithm and confusion matrix which provide fire detection accuracy around 92.71%.

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“…The algorithm is adaptive with respect to the movement of the series and reacts quickly to sudden changes. Moustafa et al [3] used three single and hybrid models, Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA), for forecasting the maximum number of blacks for cycles 25 and 26. The hyperparameters of the singular models were optimized using a Bayesian optimization approach.…”

Section: Introductionmentioning

confidence: 99%

Sunspot activity prediction based on adaptive hybrid algorithms

2024

geors

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In this paper, by combining the ARIMA model and the BP neural network model, we establish an adaptive hybrid ARIMA-BP neural network model, which provides more accurate results for sunspot prediction. For solar activity prediction, in this paper, based on the multivariate nonlinear regression and BP neural network model, we utilize the differential evolutionary algorithm for model solving and obtain satisfactory hybrid model solving results. These results provide new perspectives and methods for solar activity prediction, and provide useful references and insights for research and practice in related fields.

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Comparison of different predictive models and their effectiveness in sunspot number prediction

Cited by 14 publications

References 44 publications

Evaluating the concordance of Egyptian and international sunspot observations

Evaluating the concordance of Egyptian and international sunspot observations

Fire prediction using Machine Learning Algorithms based on the confusion matrix

Sunspot activity prediction based on adaptive hybrid algorithms

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