Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices

Garai, Sandip; Paul, Ranjit Kumar; Rakshit, Debopam; Yeasin, Md; Emam, Walid; Tashkandy, Yusra; Chesneau, Christophe

doi:10.3390/math11132896

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…Despite being major urban centers in Northern India, cities like Chandigarh and Delhi face challenges in achieving selfsustainability in agricultural production. These cities rely heavily on neighboring regions for the supply of commodities, including onions which can be prominently observed from the previous studies (Mishra et al, 2013;Jalikatti and Patil, 2015;Sinha et al, 2018;Dahiya, 2022;Garai et al, 2023). As a result, they depend on transportation from hub areas, which adds additional costs to the procurement of vegetables.…”

Section: Discussionmentioning

confidence: 90%

Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques

Shankar,

Chandel,

Gupta

et al. 2023

Front. Sustain. Food Syst.

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Modeling the arrivals and prices of agricultural commodities is an essential requirement for farmers, consumers, and governmental organizations to make informed decisions. This is particularly important for perishable commodities such as vegetables, where spoilage can lead to significant losses for farmers and have a ripple effect on supply and demand dynamics. Volatility in the arrivals and prices of vegetables like onion is a serious issue affecting the common person in different ways. The study attempts to employ different time series models like the autoregressive integrated moving average (ARIMA), Artificial neural network (ANN), hybrid, and ensemble empirical mode decomposition (EEMD) techniques to analyze the pattern and trend of onions in Chandigarh and Delhi markets. From the results of the study, the amount of volatility in the data was found to range from medium to high among the markets. Decomposition techniques such as EEMD-ARIMA and EEMD-ANN performed better for the study data with the least mean absolute percentage error (MAPE) values, such as 17.74 and 6.78% for arrivals and 9.76 and 10.24% for prices at Chandigarh and Delhi markets, respectively. The EEMD techniques exceled in handling the non-linearity and non-stationarity by decomposing the data into different intrinsic modes and a residual, providing a better understanding of the fluctuation levels of data.

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Section: Discussionmentioning

confidence: 90%

Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques

Shankar,

Chandel,

Gupta

et al. 2023

Front. Sustain. Food Syst.

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“…Here, K(x i , x) = (φ(x i ) • φ(x)) represents the kernel function and gives the dot product in hyperspace [39]. α and α * are Lagrangian multipliers, and they satisfy α i α i * = 0, i = 1, 2, .…”

Section: Support Network Regressionmentioning

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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“…Therefore, it is necessary to study noise reduction processing for UAV flight data and combine it with deep learning models to improve the Accuracy of the anomaly detection algorithm in detecting noisy data. Wavelet decomposition is a signal processing technology based on wavelet transform, which can be used to remove noise in signals and has been widely used in many research fields [43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

Zhou,

He,

Chen

et al. 2024

Aerospace

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The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.

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Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices

Cited by 12 publications

References 34 publications

Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques

Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques

European Union 2030 Carbon Emission Target: The Case of Turkey

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

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