scholarly journals ENHANCED ABC-LSSVM FOR ENERGY FUEL PRICE PREDICTION

2013 ◽  
Author(s):  
Zuriani Mustaffa ◽  
Yuhanis Yusof ◽  
Siti Sakira Kamaruddin
Keyword(s):  
Time Series Data ◽  
Back Propagation ◽  
Search Space ◽  
Back Propagation Neural Network ◽  
Series Data ◽  
Support Vector ◽  
Fuel Price ◽  
Bee Colony ◽  
Crude Oil Prices ◽  
Proposed Model

This paper presents an enhanced Artificial Bee Colony (eABC) based on Lévy Probability Distribution (LPD) and conventional mutation. The purposes of enhancement are to enrich the searching behavior of the bees in the search space and prevent premature convergence. Such an approach is used to improve the performance of the original ABC in optimizing the embedded hyper-parameters of Least Squares Support Vector Machines (LSSVM). Later on, a procedure is put forward to serve as a prediction tool to solve prediction task. To evaluate the efficiency of the proposed model, crude oil prices data was employed as empirical data and a comparison against four approaches were conducted, which include standard ABC-LSSVM, Genetic Algorithm-LSSVM (GA-LSSVM), Cross Validation-LSSVM (CV-LSSVM), and conventional Back Propagation Neural Network (BPNN). From the experiment that was conducted, the proposed eABC-LSSVM shows encouraging results in optimizing parameters of interest by producing higher prediction accuracy for employed time series data.  

scholarly journals Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2215
Author(s):  
Jung-Kai Tsai ◽  
Chih-Hsing Hung
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Classification Problem ◽  
Back Propagation Neural Network ◽  
Future Research ◽  
Support Vector ◽  
Enterprise Performance ◽  
Classification Problems ◽  
Proposed Model ◽  
Multi Class Classification

Because COVID-19 occurred in 2019, the behavioxr of humans has been changed and it will influence the business model of enterprise. Enterprise cannot predict its development according to past knowledge and experiment; so, it needs a new machine learning framework to predict enterprise performance. The goal of this research is to modify AdaBoost to reasonably predict the enterprise performance. In order to justify the usefulness of the proposed model, enterprise data will be collected and the proposed model can be used to predict the enterprise performance after COVID-19. The test data correct rate of the proposed model will be compared with some of the traditional machine learning models. Compared with the traditional AdaBoost, back propagation neural network (BPNN), regression classifier, support vector machine (SVM) and support vector regression (SVR), the proposed method possesses the better classification ability (average correct rate of the proposed method is 88.04%) in handling two classification problems. Compared with traditional AdaBoost, one-against-all SVM, one-against-one SVM, one-against-all SVR and one-against-one SVR, the classification ability of the proposed method is also relatively better for coping with the multi-class classification problem. Finally, some conclusions and future research will be discussed at the end.

scholarly journals Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xiang-ming Gao ◽  
Shi-feng Yang ◽  
San-bo Pan
Keyword(s):  
Prediction Model ◽  
Output Power ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Power Prediction ◽  
Pv System ◽  
Bee Colony

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

scholarly journals Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 218 ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Jiehao Duan ◽  
Jinyuan Liu ◽  
Fanhua Zeng
Keyword(s):  
Deep Learning ◽  
Natural Gas ◽  
Load Forecasting ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Weather Factors ◽  
Original Dataset ◽  
Proposed Model

Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.

scholarly journals Adversarial Dynamic Shapelet Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 5069-5076 ◽  
Author(s):  
Qianli Ma ◽  
Wanqing Zhuang ◽  
Sen Li ◽  
Desen Huang ◽  
Garrison Cottrell
Keyword(s):  
Time Series ◽  
Gradient Descent ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Data Sets ◽  
Training Strategy ◽  
Original Intention ◽  
Proposed Model ◽  
Adversarial Training

Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model interpretability. In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (ADSNs). An adversarial training strategy is employed to prevent the generated shapelets from diverging from the actual subsequences of a time series. During inference, a shapelet generator produces sample-specific shapelets, and a dynamic shapelet transformation uses the generated shapelets to extract discriminative features. Thus, ADSN can dynamically generate shapelets that are similar to the real subsequences rather than having arbitrary shapes. The proposed model has high modeling flexibility while retaining the interpretability of shapelet-based methods. Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods. The visualization analysis also shows the effectiveness of dynamic shapelet generation and adversarial training.

scholarly journals Prioritizing 2nd and 3rd order interactions via support vector ranking using sensitivity indices on static Wnt measurements - Part A [work in progress]

2016 ◽  
Author(s):  
shriprakash sinha
Keyword(s):  
Time Series Data ◽  
Expression Profiles ◽  
Search Space ◽  
Higher Order ◽  
Series Data ◽  
Support Vector ◽  
Data Set ◽  
Sensitivity Indices ◽  
Number Of Factors ◽  
The Individual

It is widely known that the sensitivity analysis plays a major role in computing the strength of the influence of involved factors in any phenomena under investigation. When applied to expression profiles of various intra/extracellular factors that form an integral part of a signaling pathway, the variance and density based analysis yields a range of sensitivity indices for individual as well as various combinations of factors. These combinations denote the higher order interactions among the involved factors that might be of interest in the working mechanism of the pathway. For example, in a range of fourth order combinations among the various factors of the Wnt pathway, it would be easy to assess the influence of the destruction complex formed by APC, AXIN, CSKI and GSK3 interaction. In this work, after estimating the individual effects of factors for a higher order combination, the individual indices are considered as discriminative features. A combination, then is a multivariate feature set in higher order (>2). With an excessively large number of factors involved in the pathway, it is difficult to search for important combinations in a wide search space over different orders. Exploiting the analogy of prioritizing webpages using ranking algorithms, for a particular order, a full set of combinations of interactions can then be prioritized based on these features using a powerful ranking algorithm via support vectors. The computational ranking sheds light on unexplored combinations that can further be investigated using hypothesis testing based on wet lab experiments. Here, the basic framework and results obtained on 2nd and 3rd order interactions on a toy example data set is presented. Subsequent manuscripts will examine higher order interactions in detail. Part B of this work deals with the time series data.

scholarly journals Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2538 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zhang ◽  
Xin ◽  
Liu
Keyword(s):  
Neural Network ◽  
Solar Power ◽  
Time Series Data ◽  
Short Term Memory ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Forecasting Method

The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.

scholarly journals A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-17
Author(s):  
Ping Jiang ◽  
Xuejiao Ma ◽  
Feng Liu
Keyword(s):  
Power System ◽  
Hybrid Model ◽  
Time Series Data ◽  
Electrical Power ◽  
Back Propagation ◽  
Data Preprocessing ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Electrical Power System

The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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