scholarly journals Prediction of a composite indicators using combined method of extreme learning machine and locally weighted regression

2012 ◽  
Vol 17 (2) ◽  
pp. 238-251
Author(s):  
Jurga Rukšėnaitė ◽  
Pranas Vaitkus
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Error ◽  
Composite Indicators ◽  
Weighted Regression ◽  
Combined Method ◽  
Locally Weighted Regression ◽  
Testing Sample ◽  
One Step ◽  
Learning Machine ◽  
The One

In this paper, a method of artificial neural networks (NN) is proposed as an alternative tool for the one-step-ahead prediction of composite indicators (CIs) of Lithuania’s economy. CI is composed of widely used social and economic indicators. The NN is applied for forecasting CI during the financial crisis and later periods (2008–2010) on the basis of data of earlier years (1998–2007). In this work, the Extreme Learning Machine (ELM) algorithm is combined with locally weighted regression. The analysis shows that the prediction error of a testing sample is statistically smaller compared to Levenberg–Marquardt or ELM methods.

scholarly journals Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 152 ◽  
Author(s):  
Su-qi Zhang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Local Optimum ◽  
Short Term ◽  
Swarm Optimization ◽  
Traffic Flow Forecasting ◽  
Learning Machine ◽  
The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

scholarly journals On the Structure and Estimation of Reflection Positive Processes

2008 ◽  
Vol 45 (1) ◽  
pp. 150-162
Author(s):  
R. McVinish
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Error Variance ◽  
Second Order ◽  
Prediction Error Variance ◽  
Minimum Contrast ◽  
Contrast Estimation ◽  
Special Cases ◽  
One Step ◽  
The One

The class of processes formed as the aggregation of Ornstein-Uhlenbeck processes has proved useful in modeling time series from a number of areas and includes several interesting special cases. This paper examines the second-order properties of this class. Bounds on the one-step prediction error variance are proved and consistency of the minimum contrast estimation is demonstrated.

Parameter values of ARMA models minimising the one-step-ahead prediction error when the true system is not in the model set

1983 ◽  
Vol 20 (2) ◽  
pp. 405-408 ◽  
Author(s):  
Paul Kabaila
Keyword(s):  
Prediction Error ◽  
A Priori ◽  
Arma Model ◽  
Model Parameters ◽  
Arma Models ◽  
One Step ◽  
Model Set ◽  
The One ◽  
Parameter Values ◽  
True System

In This paper we answer the following question. Is there any a priori reason for supposing that there is no more than one set of ARMA model parameters minimising the one-step-ahead prediction error when the true system is not in the model set?

scholarly journals Streamflow Prediction Improvement using Deterministic and Stochastic Wavelet-based Methods Coupled with Extreme Learning Machine

2021 ◽  
Author(s):  
Jiayu Hu ◽  
Bingjun Liu
Keyword(s):  
Extreme Learning Machine ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
East River ◽  
Comparison Results ◽  
Monthly Streamflow ◽  
Probability Prediction ◽  
Stochastic Forecasting ◽  
Learning Machine ◽  
The One

Abstract Accurate and reliable streamflow forecasting is important in hydrology and water resources planning and management. In the present work, wavelet-based direct (DF) and multi-component (MF) forecast methods performed by the à trous algorithm (AT) are proposed for both deterministic and stochastic monthly streamflow prediction improvement. They are developed in the case of the one-month lead streamflow prediction of the East River basin in China, and then compared with the benchmarks that are implemented without wavelet transform so as to evaluate the effectiveness for forecasting accuracy improvement. An existing blueprint that is flexible and practical to incorporate various sources of forecast uncertainty is extended to generate the stochastic probability prediction of streamflow. Partial mutual information is adopted for predictors selection, and six kinds of Extreme learning machine (i.e. one linear ELM and five common nonlinear kinds) are separately used as the learning algorithms coupled with the wavelet-based forecast methods to conduct a comprehensive performance evaluation. The comparison results indicate that both DF and MF can effectively increase the point prediction accuracy of monthly streamflow under deterministic and stochastic forecasting conditions, while MF performs better than DF. For stochastic prediction, it is much more reasonable to consider both parameter and model error uncertainties than just to consider only parameter uncertainty, and with the reasonable setting MF method can significantly improve the probabilistic interval prediction by greatly improving the forecast sharpness. It can be concluded that the approach using AT wavelet-based DF or MF could provide a feasible way for streamflow prediction improvement.

scholarly journals On the Structure and Estimation of Reflection Positive Processes

2008 ◽  
Vol 45 (01) ◽  
pp. 150-162
Author(s):  
R. McVinish
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Error Variance ◽  
Second Order ◽  
Prediction Error Variance ◽  
Minimum Contrast ◽  
Contrast Estimation ◽  
Special Cases ◽  
One Step ◽  
The One

The class of processes formed as the aggregation of Ornstein-Uhlenbeck processes has proved useful in modeling time series from a number of areas and includes several interesting special cases. This paper examines the second-order properties of this class. Bounds on the one-step prediction error variance are proved and consistency of the minimum contrast estimation is demonstrated.

scholarly journals Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Guangyong Gao ◽  
Caixue Zhou ◽  
Zongmin Cui
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Error ◽  
Image Distortion ◽  
Reversible Watermarking ◽  
Prediction Error Expansion ◽  
Improvement Method ◽  
Image Set ◽  
Error Expansion ◽  
Learning Machine ◽  
Embedding Distortion

Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE) technique. Firstly, the extreme learning machine (ELM) with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED) predictor and gradient-adjusted predictor (GAP), the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches.

scholarly journals Improving capability of conceptual modeling of watershed rainfall–runoff using hybrid wavelet-extreme learning machine approach

2017 ◽  
Vol 20 (1) ◽  
pp. 69-87 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Farhad Alizadeh ◽  
Vahid Nourani
Keyword(s):  
Extreme Learning Machine ◽  
Conceptual Models ◽  
Conceptual Modeling ◽  
Reservoir Modeling ◽  
Rainfall Runoff ◽  
Temporal Features ◽  
One Step ◽  
Validation Technique ◽  
Spatio Temporal ◽  
Learning Machine

Abstract Rainfall–runoff process identification, due to uncertainties and complexities, requires advanced modeling strategies. For this end, this study presented different strategies to explore spatio-temporal variation of rainfall–runoff process for the Ajichay watershed located in northwest Iran. Extreme learning machine (ELM) was used to predict the runoff in conceptual models. First, a geomorphology integrated ELM (G-ELM) was used to predict watershed runoff in multiple-stations form for the watershed. The spatial and temporal features of sub-basins were selected as input data wherein temporal features were pre-processed by wavelet transform (WT). Results confirmed the capability of G-ELM in successive prediction of watershed runoff. Afterwards, an integrated ELM (I-ELM) was developed based on conceptual reservoir modeling to predict monthly river runoff where the model had the semi-distributed specifications of ELM. This model was capable of exploring spatial variation of rainfall–runoff process without requiring physical characteristics of sub-basins. To meter sufficiency of the modeling strategies, cross-validation technique was performed for station 3 in which G-ELM performed better in comparison to I-ELMs. Furthermore, classic and wavelet-based modeling (W-ELM) of rainfall–runoff was performed for one-step-ahead predictions. Statistical evaluations confirmed the W-ELM, I-ELM, and G-ELM performance, respectively.

Parameter values of ARMA models minimising the one-step-ahead prediction error when the true system is not in the model set

1983 ◽  
Vol 20 (02) ◽  
pp. 405-408
Author(s):  
Paul Kabaila
Keyword(s):  
Prediction Error ◽  
A Priori ◽  
Arma Model ◽  
Model Parameters ◽  
Arma Models ◽  
One Step ◽  
Model Set ◽  
The One ◽  
Parameter Values ◽  
True System

In This paper we answer the following question. Is there any a priori reason for supposing that there is no more than one set of ARMA model parameters minimising the one-step-ahead prediction error when the true system is not in the model set?

Unbalanced Radial Distribution Systems Voltage Stability Index Using Extreme Learning Machine

2016 ◽  
Vol 11 (4) ◽  
pp. 428
Author(s):  
Dimas Fajar Uman Putra ◽  
Ontoseno Penangsang ◽  
Adi Soeprijanto ◽  
Hajime Miyauchi
Keyword(s):  
Extreme Learning Machine ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Stability Index ◽  
Voltage Stability Index ◽  
Learning Machine
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