Fuzzy Time Series Prediction Model Based On FCM Algorithm And Its Application

There are a lot of time series data in real life,most of them are arranged around the time axis.These data usually have certain laws.By exploring the internal laws of historical data,we can achieve the purpose of predicting time series data,and finally apply them to real life and production activities.Usually these time series data have There is a certain degree of integrity and accuracy,we call it the classical time series data.The necessary condition for the establishment of the classical time series prediction model is that the historical trend adapts to the future development.However,a large number of vague,imprecise and incomplete data still exist widely in the real world.Aiming at this kind of incomplete data,the fuzzy time series prediction model emerges as the times require.The fuzzy time series model can solve the fuzzy problem that the traditional time series prediction model cannot deal with.Its advantage lies in dealing with the uncertainty and fuzzy data,and the fuzzy time The inter sequence model has high prediction accuracy.After Zadeh put forward the fuzzy set theory in 1965,the fuzzy theory has made great achievements in the accumulation process,and has been widely used in the prediction of enrollment,stock price index,futures price,temperature and tourism number.Firstly,this paper improves the concept and prediction model of fuzzy time series proposed by song et al.The fuzzy time series prediction model proposed by song et al.Usually includes the following steps: determining and dividing the domain,defining and fuzzifying the fuzzy set,selecting the fuzzy relation,establishing the fuzzy rules,and de fuzzifying the data.However,the classical fuzzy time series prediction model usually divides the domain based on experience or data distribution,which is not reasonable,which makes the data distorted and does not make the most of the information provided by the original data.In this paper,the FCM algorithm(fuzzy c-means clustering algorithm)is used to optimize the division of the domain,making greater use of the information provided by historical data,so as to improve the prediction accuracy.The fuzzy matrix is established by the number of fuzzy relations,and the corresponding cluster center is weighted by the number of times to get the corresponding prediction value at the next moment.Finally,RMSE and MAPE are used as the evaluation criteria to evaluate the effectiveness of the model.Secondly,the model is applied to Alabama University Enrollment prediction case,and the prediction results are compared and evaluated with the results of the classic fuzzy time series prediction model.The prediction accuracy is significantly improved and better than other models.Then select the historical data of gold futures closing price from August to October 2019,and use the model to predict the closing price of gold futures in this period.From the results,it can be seen that the improved model is also applicable to the prediction of gold futures price,which provides a more valuable theory for gold futures investors and academic circles to study market rules,so as to promote the model to a wider application.Finally,the paper summarizes the improvement of the prediction model,reveals the problems and shortcomings of the model,and looks forward to the future work.Further improvement is made to combine it with more complex machine learning algorithm to achieve more accurate prediction results.While improving the prediction accuracy,the generalization ability of the model must also be considered.Multi-step prediction or highorder prediction may be a good choice.