Forecasting electricity spot price for Nord Pool market with a hybrid<i>k</i>‐factor GARMA–LLWNN model

Amor, Souhir Ben; Boubaker, Heni; Belkacem, Lotfi

doi:10.1002/for.2544

Cited by 10 publications

(6 citation statements)

References 52 publications

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“…In this study, ARMA/ARIMA is developed and also examines the performance of the model using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) (Ben Amor, Boubaker, & Belkacem, 2018;Goto & Taniguchi, 2019). As postulated by Box and Jenkins in the second half of the 1970s (Zhang et al, 2018), time series model had an autoregressive and moving average part (Gonçalves Mazzeu, Veiga, & Mariti, 2019).…”

Section: Methodsmentioning

confidence: 99%

Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics

Shobande

Shodipe

2020

Economics and Business

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This paper forecasts the world population using the Autoregressive Integration Moving Average (ARIMA) for estimation and projection for short-range and long-term population sizes of the world, regions and sub-regions. The study provides evidence that growth and population explosion will continue in Sub-Saharan Africa, tending the need to aggressively promote pragmatic programmes that will balance population growth and sustainable economic growth in the region. The study argued that early projections took for granted the positive and negative implications of population growth on the social structure and offset the natural process, which might have implication(s) on survival rate. Given the obvious imbalance in population growth across continents and regions of the world, a more purposeful inter-regional and economic co-operation that supports and enhances population balancing and economic expansion among nations is highly recommended. In this regard, the United Nations should compel member states to vigorously and effectively implement domestic and international support programmes with this objective in view.

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Section: Methodsmentioning

confidence: 99%

Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics

Shobande

Shodipe

2020

Economics and Business

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“…Initially, the historical data have been decomposed into wavelet-domain constitutive subseries using wavelet area and then using the local linear wavelet neural network (LLWNN) to form the WLLWNN forecasting model. The reader should consult Ben Amor et al (2018) and Boubaker et al (2020) for further details.…”

Section: The Wavelet Local Linear Wavelet Neural Networkmentioning

confidence: 99%

Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price

Boubaker

Bannour

2023

JRFM

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This paper aims to evaluate the forecast capability of electricity markets, categorized by numerous major characteristics such as non-stationarity, nonlinearity, highest volatility, high frequency, mean reversion and multiple seasonality, which give multifarious forecasts. To improve it, this investigation proposes a new hybrid approach that links a dual long-memory process (Gegenbauer autoregressive moving average (GARMA) and generalized long-memory GARCH (G-GARCH)) and the empirical wavelet transform (EWT) and local linear wavelet neural network (LLWNN) approaches, forming the k-factor GARMA-EWLLWNN model. The future hybrid model accomplished is assessed via data from the Polish electricity markets, and it is matched with the generalized long-memory k-factor GARMA-G-GARCH process and the hybrid EWLLWNN, to demonstrate the robustness of our approach. The obtained outcomes show that the suggested model presents important results to define the relevance of the modeling approach that offers a remarkable framework to reproduce the inherent characteristics of the electricity prices. Finally, it is presented that the adopted methodology is the most appropriate one for prediction as it realizes a better prediction performance and may be an answer for forecasting electricity prices.

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“…The ARIMA and GARMA methods are used to analyze various fields, some of which are in the fields of [8], climate and weather [9], health [10], etc. In the health sector, one of these methods is used to analyze global urgency, namely predicting positive cases of Covid-19.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Arima and Garma's Performance on Data on Positive Covid-19 Cases in Indonesia

Sofro

Khikmah²

2022

BAREKENG: J. Math. & App.

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The development of methods in statistics, one of which is used for prediction, is overgrowing. So it requires further analysis related to the goodness of the method. One of the comparisons made to the goodness of this model can be seen by applying it to actual cases around us. The real case still being faced by people worldwide, including in Indonesia, is Covid-19. Therefore, research comparing the autoregressive integrated moving average (ARIMA) and the Gegenbauer autoregressive moving average (GARMA) method in positive confirmed cases of Covid-19 in Indonesia is essential. Based on the results of this research analysis, it was found that the best model with the Aikake's Information Criterion measure of goodness that was used to predict positive confirmed cases of Covid-19 in Indonesia was the Gegenbauer autoregressive moving average (GARMA) model.

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Forecasting electricity spot price for Nord Pool market with a hybridk‐factor GARMA–LLWNN model

Cited by 10 publications

References 52 publications

Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics

Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics

Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price

Comparison of Arima and Garma's Performance on Data on Positive Covid-19 Cases in Indonesia

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