Wavelet Decomposition and Machine Learning Technique for Predicting Occurrence of Spiders in Pigeon Pea

Paul, Ranjit Kumar; Vennila, S.; Yeasin, Md; Yadav, Satish Kumar; Nisar, Shabistana; Paul, Amrit Kumar; Gupta, Ajit; Malathi, Seetalam; Jyosthna, Mudigulam Karanam; Kavitha, Zadda; Mathukumalli, Srinivasa Rao; Prabhakar, M.

doi:10.3390/agronomy12061429

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…This statement is also stated by the previous research in estimating the Czech crown/Chinese yuan exchange rate where the additional parameters can help the equalized time series to retain order and precision [29]. Besides, the Diebold Mariano (DM) test was also applied to confirm the prediction accuracy of the wavelet-ANN model to forecast the occurrence of spiders in the pigeon pea [32].…”

Section: Discussionmentioning

confidence: 59%

The soft computing FFNN method for adjusting heteroscedasticity on the time series model of currency exchange rate

Devianto

Yollanda

Maiyastri

et al. 2023

Front. Appl. Math. Stat.

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IntroductionTime series models on financial data often have problems with the stationary assumption of variance on the residuals. It is well known as the heteroscedasticity effect. The heteroscedasticity is represented by a nonconstant value that varies over time.MethodsThe heteroscedasticity effect contained in the basic classical time series model of Autoregressive Integrated Moving Average (ARIMA) can adjust its residuals as the variance model by using Generalized Autoregressive Conditional Heteroscedasticity (GARCH). In improving the model accuracy and overcoming the heteroscedasticity problems, it is proposed a combination model of ARIMA and Feed-Forward Neural Network (FFNN), namely ARIMA-FFNN. The model is built by applying the soft computing method of FFNN to replace the variance model. This soft computing approach is one of the numerical methods that can not be only applied in the theoretical subject but also in the data processing.ResultsIn this research, the accuracy of the time series model using the case study of the exchange rate United States dollar-Indonesia rupiah with a monthly period from January 2001 to May 2021 shows that the best accuracy of the possible models is the model of ARIMA-FFNN, which applies soft computing to obtain the optimal fitted parameters precisely.DiscussionThis result indicates that the ARIMA-FFNN model is better used to approach this exchange rate than the rest model of ARIMA-GARCH and ARIMA-GARCH-FFNN.

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Section: Discussionmentioning

confidence: 59%

The soft computing FFNN method for adjusting heteroscedasticity on the time series model of currency exchange rate

Devianto

Yollanda

Maiyastri

et al. 2023

Front. Appl. Math. Stat.

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show abstract

“…Artificial neural networks are mathematical models inspired by the functions of biological neural networks. Owing to the learning feature of ANNs, they can provide solutions to problems that are too complex for traditional techniques [25]. Owing to its learning ability, by using known examples, it can generalize the results to situations that have not been encountered.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

European Union 2030 Carbon Emission Target: The Case of Turkey

Kayakuş,

Terzioğlu,

Erdoğan

et al. 2023

Sustainability

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Climate awareness caused by the threat of global warming is the number one agenda item for developed and developing economies. Plans developed in this context, environmentally friendly trends in economic activities, and countries’ efforts to adapt to sustainable development have enabled new road maps. The most important of these efforts is the Paris Climate Agreement signed in 2015 and the Green Deal implemented by the European Union (EU) within the framework of this agreement. In this study, the carbon emissions of Turkey, which has important trade relations with the EU, were estimated using machine learning techniques, and a prediction was made for 2030 based on the results obtained. These results were evaluated in line with the targets of the Green Deal. The R2 of support vector regression (SVR), which was applied in the model as one of the machine learning techniques, was found to be 98.4%, and it was found to have the highest predictive power. This technique was followed by multiple linear regression (MLR) with a 97.6% success rate and artificial neural networks (ANN) with a 95.8% success rate, respectively. According to the estimates achieved with the most successful model, SVR, Turkey’s carbon emissions are expected to be 723.97 million metric tons (mt) of carbon dioxide (CO2) in 2030, the target year set by the EU. This level is 42% higher than the target that needs to be achieved given the data existing in 2019. According to these results, Turkey will not be able to reach the targets set by the EU unless it increases its coal-based energy consumption and provides incentives for renewable energy sources.

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Artificial intelligence-driven prediction system for efficient management of Parlatoria Blanchardi in date palms

Hessane,

El Youssefi,

Farhaoui

et al. 2024

Multimed Tools Appl

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Wavelet Decomposition and Machine Learning Technique for Predicting Occurrence of Spiders in Pigeon Pea

Cited by 7 publications

References 24 publications

The soft computing FFNN method for adjusting heteroscedasticity on the time series model of currency exchange rate

The soft computing FFNN method for adjusting heteroscedasticity on the time series model of currency exchange rate

European Union 2030 Carbon Emission Target: The Case of Turkey

Artificial intelligence-driven prediction system for efficient management of Parlatoria Blanchardi in date palms

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