A Univariate and multivariate machine learning approach for prediction of significant wave height

Domala, Vamshikrishna; Kim, Tae‐Wan

doi:10.1109/oceans47191.2022.9977028

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Features: These are the input variables (X) used for making predictions. Features could range from simple univariate data to complex structured data like images, text, and even video [21].…”

Section: Supervised Learningmentioning

confidence: 99%

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions

Kandaswamy,

Schwaminger

2024

Crystals

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Crystallization plays a crucial role in defining the quality and functionality of products across various industries, including pharmaceutical, food and beverage, and chemical manufacturing. The process’s efficiency and outcome are significantly influenced by solute–solvent interactions, which determine the crystalline product’s purity, size, and morphology. These attributes, in turn, impact the product’s efficacy, safety, and consumer acceptance. Traditional methods of optimizing crystallization conditions are often empirical, time-consuming, and less adaptable to complex chemical systems. This research addresses these challenges by leveraging machine learning techniques to predict and optimize solute–solvent interactions, thereby enhancing crystallization outcomes. This review provides a novel approach to understanding and controlling crystallization processes by integrating supervised, unsupervised, and reinforcement learning models. Machine learning not only improves product the quality and manufacturing efficiency but also contributes to more sustainable industrial practices by minimizing waste and energy consumption.

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Section: Supervised Learningmentioning

confidence: 99%

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions

Kandaswamy,

Schwaminger

2024

Crystals

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“…Uddin et al (2022) demonstrated the promising potential of their proposed architecture by examining data from the Dhaka Stock Exchange. In order to reduce investment risks, it is crucial for the interested parties and stakeholders to possess suitable and insightful trends of stock prices (Uddin, Alam, Das, & Sharmin, 2022) Domala and Kim (2022) predicted the significant wave height using ML and DL techniques. Regression models, SVMs, and DL models; such as LSTM and CNN; were used to conduct univariate and multivariate analyses.…”

Section: Literary Surveymentioning

confidence: 99%

“…In order to analyse the data type, exploratory data analysis was conducted on the gathered meteorological wave data. The data was then filtered and put into the ML algorithms (Domala & Kim, 2022).…”

Section: Literary Surveymentioning

confidence: 99%

Predicting Saudi Stock Market Index by Using Multivariate Time Series based on Deep Learning

Jarrah¹,

Derbali²

2023

Preprint

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Time series (TS)-based predictions are made by examining the behaviour of historical data to forecast future values. Multiple industries; such as stock market trading, power load forecasting, medical monitoring, and intrusion detection; frequently use it. Several variables; such as the performance of other markets as well as the economic situation of a country which affect its market performance; significantly affect the prediction of stock market prices. Therefore, this present study uses numerous variables; such as the opening, lowest, highest, and closing prices; to predict the indices of the stock market of the Kingdom of Saudi Arabia (KSA). Successfully accomplishing an investment goal largely depends on choosing the right stocks to buy, sell, or maintain. The project's output is the projected closing prices (regression) over the next seven days, which helps investors make the best decisions. This present study used exponential smoothing (ES) to remove noise from the input data obtained from the Saudi Stock Exchange, or Tadawul, before using a multivariate long short-term memory (LSTM) deep learning (DL) algorithm to forecast stock market prices. The proposed multivariate LSTMDL model had satisfactory prediction rates of 97.49% and 92.19% for the univariate model. Therefore, it can be used to effectively predict stock market prices. The results also indicate that DL as well as multiple sources of information can be used to accurately predict stock markets.

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Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Wu,

Zhang,

Liu

et al. 2023

2023 China Automation Congress (CAC)

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A Univariate and multivariate machine learning approach for prediction of significant wave height

Cited by 3 publications

References 12 publications

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions

Predicting Saudi Stock Market Index by Using Multivariate Time Series based on Deep Learning

Significant Wave Height Prediction Based on VMD-SA-MLP-BP

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