A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Combined forecast method of HMM and LS-SVM about electronic equipment state based on MAGA

For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.

Multi-variable Dependent Forecast Algorithm for Predicting Secondary Arc

Hybrid reactive power compensation(HRPC)combines step-controlled shunt reactors and series compensation,and will be employed in ultra-high-voltage(UHV)power systems.The single-phase auto-reclosure characteristics of secondary arcs in systems with HRPC require further investigation.In this paper,both the arc-recalling voltage and subsidiary variations in arc current are investigated with and without HRPC.The frequency components of the secondary arc current and variations in arcing time are analyzed for various influential factors,such as the neutral reactor,arc resistance,fault location,degrees of compensation of HRPC,and the length of the transmission line.The non-dominated sorting genetic algorithm II(NSGA-II)and support vector machine regression are combined to create a multi-variable dependent forecasting algorithm to predict the characteristics of the secondary arc in UHV systems with HRPC.This paper provides a theoretical reference for optimizing the parameters of HRPC,and for developing adaptive auto-reclosure schemes and protection equipment.