An improved fuzzy time series forecasting model using variations of data

Vovan, Tai

doi:10.1007/s10700-018-9290-7

Cited by 33 publications

(16 citation statements)

References 20 publications

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“…The proposed algorithm outperforms the existing ones in terms of MSE and AFER; thereby proving to be a best fit for wheat produce prediction. The MSE and AFER of the proposed algorithm comes out to be 362,119.88 and 5,107,713.738 for 3rd degree and 2nd degree polynomial in 9th interval as compared to the MSE of 36,559.88 and AFER AS 11.92547975 for 3rd degree polynomial of Yalaz et al [64]. Similarly, the values of MSE and AFER are compared in Tables 13 and 14 for 9th interval 2nd degree polynomial.…”

Section: Resultsmentioning

confidence: 86%

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Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Jain

Kapania

et al. 2018

Symmetry

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Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

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Section: Resultsmentioning

confidence: 86%

“…Definition 5 [64]. Given F(t) as the time series data D, with Ft(I) as fuzzy set, a quasi-arithmetic mean for fuzzified output is: Definition 6 [65].…”

Section: Mathematical Preliminarymentioning

confidence: 99%

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Jain

Kapania

et al. 2018

Symmetry

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“…Further improvements to the original algorithm were proposed in [18,19]. Later, various options for improving the efficiency of fuzzy time series forecasting algorithms were proposed [20][21][22][23][24][25][26].…”

Section: Literature Review About Fuzzy Time Series Forecastingmentioning

confidence: 99%

Analysis of Fuzzy Time Series Forecasting for Migration Flows

Uzhga-Rebrov

Grabusts

2022

Symmetry

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The goal of this article is to forecast migration flows in Latvia. In comparison with many other countries with sufficiently symmetric emigration and immigration flows, in Latvia, migration flows are very asymmetric: the number of emigrants considerably exceed the number of immigrants. Since statistical data about migration are usually inaccurate, we employ fuzzy time series forecasting methods for prognosticating migration flows in Latvia forecasting. The use of this type of method is often useful not only for forecasting purposes. Three different methods for fuzzy time series forecasting are used. A detailed comparative analysis of the obtained results is given. Generalized forecasts of the expected net migration flow in the future are presented.

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“…Rana [10] presented a study for FTS models for forecasting rice production. The data variation is used by Vovan [20] and developed an improved FTSM for forecasting based on it.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Fuzzy Models for Crop Production Forecasting

Rana¹

2020

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Fuzzy sets theory is a very useful technique to increase effectiveness and efficiency of forecasting. The conventional time series is not applicable when the variable of time series are words variables i.e. variables with linguistic terms. As India and most of the Asian countries are of agriculture-based economy with very smaller farmer land holding area in comparison to America, Australia and Europe counterparts, it becomes more important for these countries to have an approximate idea regarding future crop production. It not only will help in planning policies for future but also will be a great help for farmers and agro based companies for their future managements. For small area production, soft computing technique is an important and effective tool for predicting production, as agriculture production involve a high degree of uncertainties in many parameters. In the present study, 21 years agricultural crop yield data is used and a comparative analysis of forecast is done with three fuzzy models. The robustness of the model is tested on real time agricultural farm production data of wheat crop of G.B. Pant University of Agriculture and Technology Pantnagar, India. As soft computing techniques involve uncertainty of the system under study, it becomes more and more important for forecasting models to be accurate with the prediction. The efficiency of the three models is examined on the basis of statistical errors. The models under study are judged on the basis of Mean Square Error and average percentage error. The results of the study are in case of small area production prediction and will encourage for predicting large scale production.

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An improved fuzzy time series forecasting model using variations of data

Cited by 33 publications

References 20 publications

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Analysis of Fuzzy Time Series Forecasting for Migration Flows

Comparative Study on Fuzzy Models for Crop Production Forecasting

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