Hybrid Forecasting Model of Reactive Load Based on FOA and Linear Exponential Smoothing

2013 ◽  
Vol 823 ◽  
pp. 500-504
Author(s):  
Yuan Sheng Huang ◽  
Te Li ◽  
Wei Pi
Keyword(s):  
Prediction Accuracy ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Hybrid Forecasting ◽  
Parameter Values

During the prediction of Linear exponential smoothing model,the value of the coefficient has a little blindness,which is always valued by experience.This paper uses FOA model to optimize it in Linear exponential smoothing model,constructing the hybrid forecasting model this paper uses to predict the reactive load of a substation.Then,this paper uses the hybrid forecasting model to forecast reactive load.The results show that:compared with the traditional Linear exponential smoothing model,the hybrid forecasting model is effective not only on selecting parameter values ,but also improving the prediction accuracy of the reactive load to a big extent.

scholarly journals Hybrid forecasting model for non-linear time series

2020 ◽  
Author(s):  
E. Priyadarshini ◽  
G. Raj Gayathri ◽  
M. Vidhya ◽  
A. Govindarajan ◽  
Samuel Chakkravarthi
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Forecasting Model ◽  
Non Linear ◽  
Hybrid Forecasting

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

A hybrid forecasting model for ageing breast deformation during yoga practice

2021 ◽  
pp. 004051752110408
Author(s):  
Jie Zhou ◽  
Jianming Chen ◽  
Newman Lau ◽  
Qian Mao ◽  
Zidan Gong ◽  
...  
Keyword(s):  
Left Breast ◽  
Forecasting Model ◽  
Yoga Practice ◽  
Grey Relation Analysis ◽  
Senior Women ◽  
Hybrid Forecasting ◽  
Learning Machine ◽  
The Right ◽  
Grey Relation ◽  
Motion Amplitude

In this work, the deformation of bilateral breasts was investigated with an established hybrid model to predict the nipple movement specifically for senior women during yoga exercise. A motion capture system was used to collect the displacement of 10 markers on the breasts from 11 senior women (average age of 62) during yoga practice and then the data were analyzed by integrating the absolute grey relation analysis (AGRA) and extreme learning machine (ELM). The right and left breasts had the maximum motion amplitude in the horizontal direction but they were respectively featured with contraction and extension during yoga practice. AGRA showed that the nipple motion was highly associated with the vertical region above the nipple for the left breast but the parallel region along with the nipple for the right breast. The ELM model is able to predict the nipple movement within tolerable error (∼0.0037). This study lays a foundation for a better understanding of ageing breast kinematics during yoga poses with limited practical experiments. Besides, the accurate and efficient results can be used not only for yoga pose instruction but also for ergonomic sports bra design.

scholarly journals Foundation Settlement Prediction Based on a Novel NGM Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Peng-Yu Chen ◽  
Hong-Ming Yu
Keyword(s):  
Prediction Accuracy ◽  
Forecasting Model ◽  
Foundation Settlement ◽  
Engineering Construction ◽  
Settlement Prediction ◽  
Exponential Law ◽  
Grey Forecasting ◽  
Index Sequence ◽  
Settlement Time ◽  
Time Sequences

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM(1,1,k,c)model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM(1,1,k,c)model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM(1,1,k,c)model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

A Hybrid Forecasting Model for Non-Stationary Time Series

2012 ◽  
Vol 3 (2) ◽  
pp. 67-82 ◽  
Author(s):  
Yi Xiao ◽  
Jin Xiao ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stationary Time Series ◽  
Adaptive Mutation ◽  
Forecasting Model ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Individual Fitness ◽  
Hybrid Forecasting

In time series analysis, an important problem is how to extract the information hidden in the non-stationary and noise data and combine it into a model for forecasting. In this paper, the authors propose a TEI@I based hybrid forecasting model. A novel feed forward neural network is developed based on the improved particle swarm optimization with adaptive genetic operator (IPSO-FNN) for forecasting. In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Subsequently, a crossover rate which only depends on generation and an adaptive mutation rate based on individual fitness are designed. The parameters of FNN are optimized by binary and decimal particle swarm optimization. Further, the forecast results of IPSO-FNN are adjusted with the knowledge from text mining and an expert system. The empirical results on the container throughput forecast of Tianjin Port show that forecasts with the proposed method are much better than some other methods.

Predicting the NHL playoffs with PageRank

2017 ◽  
Vol 13 (4) ◽  
Author(s):  
Nathan Swanson ◽  
Donald Koban ◽  
Patrick Brundage
Keyword(s):  
Prediction Accuracy ◽  
Cross Validation ◽  
Markov Models ◽  
Limited Evidence ◽  
Tuning Parameters ◽  
Improved Performance ◽  
Parameter Values ◽  
Fold Cross Validation

AbstractApplying Google’s PageRank model to sports is a popular concept in contemporary sports ranking. However, there is limited evidence that rankings generated with PageRank models do well at predicting the winners of playoffs series. In this paper, we use a PageRank model to predict the outcomes of the 2008–2016 NHL playoffs. Unlike previous studies that use a uniform personalization vector, we incorporate Corsi statistics into a personalization vector, use a nine-fold cross validation to identify tuning parameters, and evaluate the prediction accuracy of the tuned model. We found our ratings had a 70% accuracy for predicting the outcome of playoff series, outperforming the Colley, Massey, Bradley-Terry, Maher, and Generalized Markov models by 5%. The implication of our results is that fitting parameter values and adding a personalization vector can lead to improved performance when using PageRank models.

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