A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption

Energy ◽  
2020 ◽  
Vol 197 ◽  
pp. 117200 ◽  
Author(s):  
Fazil Kaytez
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Hybrid Approach ◽  
Electricity Consumption ◽  
Least Square ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Long Term Forecasting

scholarly journals Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

2014 ◽  
Vol 18 (7) ◽  
pp. 2711-2714 ◽  
Author(s):  
F. Fahimi ◽  
A. H. El-Shafie
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Water Resource Management ◽  
River Flow ◽  
Moving Average ◽  
Least Square ◽  
Support Vector ◽  
Self Organizing Map ◽  
River Flow Forecasting ◽  
Self Organizing

Abstract. Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques that have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012), which was published in the journal Hydrology and Earth System Sciences in 2012, was a valuable piece of research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of self organizing map and least square support vector machine (SOM-LSSVM), autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

scholarly journals Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

2013 ◽  
Vol 10 (11) ◽  
pp. 13889-13895
Author(s):  
F. Fahimi ◽  
A. H. El-Shafie
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Water Resource Management ◽  
River Flow ◽  
Moving Average ◽  
Least Square ◽  
Support Vector ◽  
Self Organizing Map ◽  
River Flow Forecasting ◽  
Self Organizing

Abstract. Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques, which have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012) which has been published in Journal of Hydrology and Earth System Sciences in 2012 was a valuable research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of SOM-LSSVM, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

Time Series Forecasting using Least Square Support Vector Machine for Canadian Lynx Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Shuhaida Ismail ◽  
Ani Shabri
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Support Vector Machine ◽  
Moving Average ◽  
Absolute Error ◽  
Research Area ◽  
Time Series Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Promising Alternative

Time series analysis and forecasting is an active research area over the last few decades. There are various kinds of forecasting models have been developed and researchers have relied on statistical techniques to predict the future. This paper discusses the application of Least Square Support Vector Machine (LSSVM) models for Canadian Lynx forecasting. The objective of this paper is to examine the flexibility of LSSVM in time series forecasting by comparing it with other models in previous research such as Artificial Neural Networks (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Feed-Forward Neural Networks (FNN), Self-Exciting Threshold Auto-Regression (SETAR), Zhang’s model, Aladang’s hybrid model and Support Vector Regression (SVR) model. The experiment results show that the LSSVM model outperforms the other models based on the criteria of Mean Absolute Error (MAE) and Mean Square Error (MSE). It also indicates that LSSVM provides a promising alternative technique in time series forecasting.

Modeling River Stream Flow Using Support Vector Machine

2013 ◽  
Vol 315 ◽  
pp. 602-605 ◽  
Author(s):  
Ali Rafidah ◽  
Yacob Suhaila
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Stream Flow ◽  
Moving Average ◽  
Support Vector ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Svm Model ◽  
Record Data ◽  
Better Than

Support Vector Machine (SVM) is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in river stream flow forecasting. In this paper, SVM is proposed for river stream flow forecasting. To assess the effectiveness SVM, we used monthly mean river stream flow record data from Pahang River at Lubok Paku, Pahang. The performance of the SVM model is compared with the statistical Autoregressive Integrated Moving Average (ARIMA) and the result showed that the SVM model performs better than the ARIMA models to forecast river stream flow Pahang River.

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