scholarly journals Prediction of Indian Monsoon Rainfall by Interval based Simplified High Order Fuzzy Time Series Model

2020 ◽  
Vol 9 (4) ◽  
pp. 782-785
Keyword(s):  
Time Series ◽  
Monsoon Rainfall ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Time Data ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Soft Computing Techniques ◽  
Predicted Values ◽  
High Degree

Rain is of uttermost importance for agriculture based economies. Most of the Asian countries, India in particular largely depend on a good rainfall. The prediction of rainfall will not only help government to make better future policies but also farmers and agro based companies can make better future management. Rainfall forecasting involves high degree of uncertainty and for such conditions fuzzy time series and other soft computing techniques are best to deal with. The utility of a forecasting method lies with the accuracy with the predicted values. In this paper rainfall prediction by fuzzy time series model is proposed in which two difference values of the interval corresponding to the fuzzified forecasted value is proposed. This model is tested on real time data of average monsoon rainfall in India. The predicted values are compared with Chen model. The results show that the proposed model have less error compared to Chen’s model.

About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

2011 ◽  
Vol 3 (9) ◽  
pp. 562-566
Author(s):  
Ramin Rzayev ◽  
◽  
Musa Agamaliyev ◽  
Nijat Askerov
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Fuzzy Time Series

scholarly journals FUZZY TIME SERIES MODEL CHENG UNTUK MERAMALKAN VOLUME HASIL PANEN PADA TANAMAN GARUT

Telematika ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 11
Author(s):  
Rifki Indra Perwira ◽  
Danang Yudhiantoro ◽  
Endah Wahyurini
Keyword(s):  
Time Series ◽  
Seed Quality ◽  
Crop Yields ◽  
Genetic Data ◽  
Time Series Model ◽  
Environmental Data ◽  
Fuzzy Time Series ◽  
Market Demand ◽  
Cultivation Technique ◽  
Processed Products

Arrowroot is an alternative food substitute that can be used as processed flour or starch. This arrowroot can also produce several processed products such as arrowroot chips. The number of arrowroot requests from various regions causes the need for accurate calculations related to the volume of harvest from the arrowroot. Fuzzy logic is a method that can be used to predict arrowroot yields every period to meet market demand. The parameters used in this system are based on environmental data (temperature humidity, climate, altitude), genetic data (age and variety), and cultivation technique data (seed quality, fertilizing, planting media). The results of this study are in the form of an application to predict the volume of arrowroot crop yields based on these parameters. From the results of MAPE, get a percentage of 11.7% which indicates that the level of accuracy using the fuzzy cheng time series model is said to be useful for forecasting on arrowroot plants.

An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data

2018 ◽  
pp. 1773-1791 ◽  
Author(s):  
Prateek Pandey ◽  
Shishir Kumar ◽  
Sandeep Shrivastava
Keyword(s):  
Time Series ◽  
Performance Comparison ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Data Set ◽  
Common Trend ◽  
Proposed Model ◽  
Forecasting Method ◽  
High Degree ◽  
Degree Of Instability

In recent years, there has been a growing interest in Time Series forecasting. A number of time series forecasting methods have been proposed by various researchers. However, a common trend found in these methods is that they all underperform on a data set that exhibit uneven ups and downs (turbulences). In this paper, a new method based on fuzzy time-series (henceforth FTS) to forecast on the fundament of turbulences in the data set is proposed. The results show that the turbulence based fuzzy time series forecasting is effective, especially, when the available data indicate a high degree of instability. A few benchmark FTS methods are identified from the literature, their limitations and gaps are discussed and it is observed that the proposed method successfully overcome their deficiencies to produce better results. In order to validate the proposed model, a performance comparison with various conventional time series models is also presented.

A new fuzzy time-series model of fuzzy number observations

1995 ◽  
Vol 73 (3) ◽  
pp. 341-348 ◽  
Author(s):  
Qiang Song ◽  
Robert P. Leland ◽  
Brad S. Chissom
Keyword(s):  
Time Series ◽  
Fuzzy Number ◽  
Time Series Model ◽  
Fuzzy Time Series
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