Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Ali, Ahmed; Fathalla, Ahmed; Salah, Ahmad; Bekhit, Mahmoud; Eldesouky, Esraa

doi:10.1155/2021/8551167

Cited by 19 publications

(15 citation statements)

References 43 publications

(58 reference statements)

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“…DL techniques, in particular recurrent neural networks (RNNs), have been shown to be effective in a variety of applications, one of which is time series forecasting, where they have been utilized [2,6,15]. Diverse applications, including time series forecasting, have demonstrated the effectiveness of DL approaches.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Recent improvements in ML and DL structures have exhibited state-of-the-art performance in a variety of applications [6,7], such as text processing, pictures, speech, and audio on a variety of natural language processing (NLP) and computer vision applications, which include language modeling [8], speech recognition [9,10], computer vision [11], sentence classification [12] and machine translation [13]. Typically, ML and DL have become innovative approaches for financial data analysis in recent years.…”

Section: Introductionmentioning

confidence: 99%

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Price Prediction of Seasonal Items Using Time Series Analysis

Salah¹,

Bekhit²,

Eldesouky³

et al. 2023

Computer Systems Science and Engineering

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The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items' prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items' prices as a time series task. We proposed utilizing two different approaches to address this problem, namely, 1) machine learning (ML)-based models and 2) deep learning (DL)-based models. Thus, this research tuned a set of well-known predictive models on a real-life dataset. Those models are ensemble learning-based models, random forest, Ridge, Lasso, and Linear regression. Moreover, two new DL architectures based on gated recurrent unit (GRU) and long short-term memory (LSTM) models are proposed. Then, the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics, where the evaluation includes both numerical and visual comparisons of the examined models. The obtained results show that the ensemble learning models outperformed the classic machine learning-based models (e.g., linear regression and random forest) and the DL-based models.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Price Prediction of Seasonal Items Using Time Series Analysis

Salah¹,

Bekhit²,

Eldesouky³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…In this study, we utilize the power of deep learning models in order to forecast the daily OPEC oil price forecasting. Deep learning models have contributed and achieved lots of impressive results in different domains [42][43][44][45]. Among the newest powerful classes of deep learning models is the transformer model [46].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

Fathalla¹,

Alameer²,

Abbas³

et al. 2023

Computer Systems Science and Engineering

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The oil industries are an important part of a country's economy. The crude oil's price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil's price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries (OPEC) oil price forecasting. Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods. More precisely, the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA (1,1,1), ARIMA (0,1,1), autoregressive moving average (ARMA) (0,1), vector autoregression (VAR), random walk (RW), support vector machine (SVM), and random forests (RF) models by 99.94%, 99.62%, 99.87%, 99.65%, 7.55%, 98.38%, and 99.35%, respectively, according to mean square error metric.

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“…Therefore, data-driven methods like time series models and machine learning algorithms have recently acquired great attention for SWH estimation to improve prediction performance by digging into the inherent characteristics of historical data and reducing the reliance on prior knowledge. Experimental results have revealed a better performance of machine learning methods over the statistical predictive model in SWH prediction [17].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height

Liu

Sun

et al. 2022

IEEE Access

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Predicting significant wave height (SWH) is significant for coastal energy evaluation and utilization, port construction, and shipping planning. It has been reported that SWH is difficult to forecast for the complex marine conditions and chaos in nature. Current methods either require reliable prior information or reach the upper limit of prediction accuracy. To this end, this paper proposes a wavelet-based residual network to predict SWH with high accuracy. First, the time-series data of wave-related factors collected by the ocean buoy station is decomposed using the wavelet transformation. Then, the transformation results are used as the inputs to train the residual neural network. Finally, the data obtained from the NOAA's National Data Buoy Center is used to prove the outperformed prediction accuracy of the proposed method. The analysis results suggested that wavelet transformation can improve the prediction performance of the neural network, and the proposed model achieves better performance compared with several other deep neural network schemes.

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Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Cited by 19 publications

References 43 publications

Price Prediction of Seasonal Items Using Time Series Analysis

Price Prediction of Seasonal Items Using Time Series Analysis

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height

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