Analysis of Wind Speed Prediction using Artificial Neural Network and Multiple Linear Regression Model using Tinyml on Esp32

doi:10.37934/arfmts.107.1.2944

Cited by 2 publications

(2 citation statements)

References 14 publications

(23 reference statements)

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“…However, RQ and Per has extra hyperparameter on the equation which is positive value of scale-mixture parameter, 𝛼 while Per, has another extra hyperparameter which is periodicity, 𝑝. The equations of RBF, RQ, Per, Mat 3/2 and Mat 5/2 are expressed in Equations ( 8), ( 9), ( 10), (11), and (12).…”

Section: Covariance Functionsmentioning

confidence: 99%

“…The statistical model commonly used in the data series is Autoregressive Integrated Moving Average (ARIMA), while the machine learning model consists of Random Forest, Adaboost, and Gradient Boost [11]. Deep learning models are a subset of machine learning that can be used to predict time series data consisting of Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP) networks, and Long Short-Term Memory (LSTM) networks [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study on the Performance of Covariance Functions in Gaussian Process Regression model: Application to Global Wheat Price

Nahamizun Maamor,

Hanita Daud,

Muhammad Naeim Mohd Aris

2024

ARASET

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Gaussian Process Regression (GPR) is a nonparametric machine learning model that provides uncertainty quantification in making predictions. GPR utilizes several covariance functions (CFs) in the process of developing models to ensure high accuracy. There are five common CFs in GPR, which are the Radial Basis Function (RBF), Rational Quadratic (RQ), Periodic (Per), Matérn 3/2 (Mat 3/2), and Matérn 5/2 (Mat 5/2), where each covariance function (CF) has different characteristics and behaviors. This paper is to investigate the comparative performances of each CF when applied to the Global Wheat Prices dataset. Error metric measurement like Mean Square Error (MSE) and Root Mean Square Error (RMSE), while Coefficient of Variation (CV) for uncertainty quantification measurement is computed for each CF, and comparisons was made among the CFs to conclude the best CF for this application. The lowest values among them will be the best CF for the data. It should be noted that the CV for each model should be less than 5%, and the CF with the smallest value of CV is considered reliable. The five CFs were fit to the Global Wheat Prices dataset, and it was found that the Mat 3/2 produced the best performances with the lowest values of MSE, RMSE, and CV. Mat 3/2 is the most efficient CF for making predictions since it gives the lowest value of error metric measurement and the lowest value of CV under 5% among the other CFs, making it more reliable for modeling. Overall, the outcome shows that Mat 3/2 is the best CF to be used in developing a GPR model to predict Global Wheat Prices dataset.

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Section: Covariance Functionsmentioning

confidence: 99%