Multistep-ahead forecasting of coal prices using a hybrid deep learning model

Alameer, Zakaria; Fathalla, Ahmed; Li, Kenli; Ye, Haiwang; Zhang, Jianhua

doi:10.1016/j.resourpol.2020.101588

Cited by 97 publications

(44 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second is based on the development of cryptocurrencies with an emphasis on futures pricing behaviour, while finally, the third area through which we develop our work is based on several pieces that have examined the predictability of cryptocurrency spot prices. Primarily, machine learning has been used across a variety of areas such as that of stock markets (Wittkemper and Steiner 1996;Ntakaris et al 2018;Sirignano 2019;Huck 2019;Sirignano and Cont 2019;Huang and Liu 2020;Philip 2020); currency markets during crises (El Shazly and El Shazly 1999;Zimmermann et al 2001;Auld and Linton 2019); energy markets such as West Texas Intermediate (Chai et al 2018), crude oil markets (Fan et al 2016), Cushing oil and gasoline markets (Wang et al 2018), gold markets (Chen et al 2020); gas markets (Ftiti et al 2020), agricultural futures (Fang et al 2020); copper markets (Sánchez Lasheras et al 2015); and coal markets (Matyjaszek et al 2019;Alameer et al 2020); cryptocurrency spot markets Chowdhury et al 2020;Chen et al 2021) options markets (Lajbcygier 2004;De Spiegeleer et al 2018); and futures markets (Kim et al 2020).…”

Section: Previous Literaturementioning

confidence: 99%

Forecasting mid-price movement of Bitcoin futures using machine learning

et al. 2021

View full text Add to dashboard Cite

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

show abstract

Section: Previous Literaturementioning

confidence: 99%

Forecasting mid-price movement of Bitcoin futures using machine learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Ewees et al [2] proposed a hybrid intelligent model i.e., chaotic grasshopper optimization algorithm-ANN for estimation of iron ore prices and concluded that their proposed model is a promising technique for forecasting commodity prices with high accuracy. In another study of price prediction, the prediction of coal price fluctuations was considered as the objective by Alameer et al [83]. They developed three models namely support vector machine (SVM), ANN, and deep neural network (DNN) with the aim to successfully show that the DNN model is able to provide higher performance capacity in estimating coal price fluctuations.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Moghaddam

Monjezi

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.

show abstract

“…Machine learning and its applications have proven their success in many applications [ 8 , 9 , 10 ]. Therefore, in this paper, several machine learning models have been utilized, including DT, GNB, KNN, and gradient boosting.…”

Section: Introductionmentioning

confidence: 99%

A Robust UWSN Handover Prediction System Using Ensemble Learning

Eldesouky

Bekhit

Fathalla

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

show abstract

Multistep-ahead forecasting of coal prices using a hybrid deep learning model

Cited by 97 publications

References 62 publications

Forecasting mid-price movement of Bitcoin futures using machine learning

Forecasting mid-price movement of Bitcoin futures using machine learning

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

A Robust UWSN Handover Prediction System Using Ensemble Learning

Contact Info

Product

Resources

About