Combination Forecasting of Fuzzy Forecast

Ji, Ai-bing; Qiao, Yu; Qiu, Hong-jie

doi:10.1109/fskd.2009.631

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Figure 2 is the entire dataset. Next, do the forecasting process with the k-NN method where the first process of forecasting determines the value of k using the normalized Euclidean distance as in (1). The forecasting process is carried out based on 165 testing data and 2,200 training data with detailed training from 1 January 2010 to 30 December 2019 and testing data from 1 January 2020 to 25 August.…”

Section: Resultsmentioning

confidence: 99%

“…Forecasting is a technique of data mining combined with machine learning that is used to analyze and calculate future events by using reference to past data with qualitative and quantitative approaches [1][2][3]. Forecasting has the objective of estimating prospects for economic progress as well as business activities and the environmental impact on these prospects [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Al-Khowarizmi

Syah

Nasution

et al. 2021

IJECE

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Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Al-Khowarizmi

Syah

Nasution

et al. 2021

IJECE

View full text Add to dashboard Cite

show abstract

“…Compared with the single method, the prediction effect was better, which proved the practicality of combined prediction. Literature [24] studied the fuzzy combination model, and proposed linear and non-linear fuzzy combination forecasts.…”

Section: Related Workmentioning

confidence: 99%

Forecasting of Musical Equipment Demand Based on a Deep Neural Network

Zhang

2022

Mobile Information Systems

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With the popularity of music, the music equipment market has ushered in a new round of explosion. But at the same time, the market environment is changing rapidly, and music equipment companies are greatly affected by market changes. This type of enterprise has a wide variety of products, fast update, short delivery time and urgent time, which is a severe challenge to its production and operation. In this environment, in order to quickly respond to changes in the market environment, and to formulate plans for corporate procurement, production, and sales in an orderly manner, music equipment companies must make accurate predictions of market demand. At present, the extended research based on LSTM is a relatively mainstream deep neural network in the research methods of time series problems. This article is based on LSTM model to conduct an in-depth study on the prediction of music equipment demand. In order to solve the problems of overfitting, disappearance of gradient, model collapse and other problems in previous experimental studies, this paper proposes an improved LSTM prediction model. In terms of model structure selection, Dropout mechanism is used, and L2 regular term is introduced. In the selection of the activation function, MReLU function is proposed, which can improve the prediction effect of the model and enhance the applicability of the model. To measure the prediction effect of the improved LSTM model established in this paper, this paper selects RMSE and MAE as evaluation indicators, and compares experiments with other mainstream prediction models. The research results show that the improved LSTM network prediction model is superior to other models in the prediction of music equipment demand, which verifies the effectiveness.

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“…Considering the standard deviation of predictive precision, H. Y. Chen [7] developed a combination forecasting model for better precision of prediction, and the weight coefficients of combination forecasting is calculated by linear programming. At present, different weighted means are assigned to each single forecasting method [8][9][10][11][12][13][14][15][16][17][18][19][20], and the common nature is that the structure of forecasting models is fixed no matter what the values of weight coefficient are. In 1963, the original SVM (support vector machine) was invented by Vladimir N. Vapnik and Alexey Ya.…”

Section: Introductionmentioning

confidence: 99%

Combination Forecasting Model for Predicting the Shelf Life of Two-state Materials Based on Support Vector Machine

Li¹,

Gao²,

Shenyang³

et al. 2015

TOMSJ

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A combination forecasting model based on Support Vector Machine (SVM) whose objective is to minimize the structure risk, is proposed. The storage failure of two-state materials tends to fail immediately without any recognizable defeats prior to the failure, which increases the difficulty of forecasting, so the combination forecasting model is often used to optimize the prediction effect. The core ideas of previous combination forecasting models such as those based on forecasting error and those based on nonlinear weighted average are finding the optimal weights, but the structure of forecasting model is fixed. In this paper, three single forecasting models, Weibull distribution statistic method, BP neural network prediction method and SPFM (Sliding Polynomial Fitting Method) are chosen in which their forecast mechanisms are completely different. The results of single forecasting methods are used as training set of SVM. By using libsvm toolbox, we can get the nonlinear mapping functions that have the minimum structure risk. At last, a simulation is conducted to verify this model by using the data from Petroleum Center.

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Combination Forecasting of Fuzzy Forecast

Cited by 6 publications

References 12 publications

Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Forecasting of Musical Equipment Demand Based on a Deep Neural Network

Combination Forecasting Model for Predicting the Shelf Life of Two-state Materials Based on Support Vector Machine

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