Fuzzy model translation for time series data in the extent of median error and its application

Nurhayadi, Nurhayadi; Subanar, .; Abdurakhman, Abdurakhman; Abadi, Agus Maman

doi:10.12988/ams.2014.42114

Cited by 3 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Takaki-Sugeno-Kang relation is created, if 𝐴 𝑖𝑗 has a relation with some crisp number 𝑦 𝑖𝑗 1 , 𝑦 𝑖𝑗 2 , ... , 𝑦 𝑖𝑗 𝑛 then 𝐴 𝑖𝑗 related to the median 𝑦 ̃𝑖𝑗 ; j = 1, 2, ...n in the hope of obtaining a time series forecast with the smallest absolute error (Nurhayadi et al, 2014), so that for each j, 𝑦 𝑖,𝑡 can be calculated:…”

Section: 𝑌 𝑖(𝑡−1) → 𝑌 𝑖𝑡mentioning

confidence: 99%

Fuzzy Vector Autoregression for Forecasting Farmer Exchange Rate in Central Java Province

Nurhayadi

2022

Medstat

View full text Add to dashboard Cite

Computer technology has developed to a very advanced measure. Calculations using complex formulas are no longer an obstacle for industry and researchers. Along with advances in computing technology, the development of fuzzy system models is also experiencing rapid progress. This paper proposes a fuzzy model combined with Vector Autoregression. The fuzzy membership function is built by selecting the median of each set to be the center of the fuzzy set. The function chosen as the membership function is Gaussian. The fuzzy Vector Autoregression model obtained was applied to the Farmer's Exchange Rate in Central Java Province. The accuracy of the model is measured based on the Mean Absolute Percentage Error. The results of model trials on FER Central Java in 2014-2020, show a pretty good forecast, namely forecasting with MAPE around 5%, and not exceeding 10%.

show abstract

Section: 𝑌 𝑖(𝑡−1) → 𝑌 𝑖𝑡mentioning

confidence: 99%

Fuzzy Vector Autoregression for Forecasting Farmer Exchange Rate in Central Java Province

Nurhayadi

2022

Medstat

View full text Add to dashboard Cite

show abstract

“…In order to increase the accuracy, the model needs to be given some advanced treatments. Nurhayadi et al (2014) …”

Section: Translation Of Fuzzy Modelmentioning

confidence: 99%

“…Song and Chissom (1993a;1993b; have studied fuzzy application to predict university enrollment. Time series using fuzzy model has been applied to predict peak load electricity demand (Ismail et al, 2009) and has been applied to predict stock price (Egrioglu, 2014;Kao et al, 2013;Nurhayadi et al, 2014;Singh and Borah, 2014). Rodger (2014) has used fuzzy model to predict the need of natural gas and the energy cost savings in public buildings.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Model Optimization for Time Series Data Using a Translation in the Extent of Mean Error

Nurhayadi¹,

Subanar²,

Abdurakhman³

et al. 2014

Journal of Mathematics and Statistics

View full text Add to dashboard Cite

Recently, many researchers in the field of writing about the prediction of stock price forecasting, electricity load demand and academic enrollment using fuzzy methods. However, in general, modeling does not consider the model position to actual data yet where it means that error is not been handled optimally. The error that is not managed well can reduce the accuracy of the forecasting. Therefore, the paper will discuss reducing error using model translation. The error that will be reduced is Mean Square Error (MSE). Here, the analysis is done mathematically and the empirical study is done by applying translation to fuzzy model for enrollment forecasting at the Alabama University. The results of this analysis show that the translation in the extent of mean error can reduce the MSE.

show abstract

“…A fuzzy time series model has been applied to predict d electricity demand's peak load [6]. It has been applied to predict the stock price [7][8][9][10]. Rodger [11] has used the fuzzy model to predict the need for natural gas and the cost of saving energy in public buildings.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Average-Based Fuzzy Time Series Model in Forecasting Product Selling at Ainaya Boutique

Ramadhani¹,

Abadi²

2021

Advances in Social Science, Education and Humanities Research

View full text Add to dashboard Cite

Demand forecasting is needed in a company to find out the estimated level of demand in the future. Forecasting cannot be considered as an absolute one because there is no accurate forecasting. The forecasting accuracy level continues to be sought through forecasting development. One of those is applied through artificial intelligence. This study discusses the application of forecasting product selling using average-based fuzzy time series in Ainaya Boutique as the case of the study. The forecasting system with average-based fuzzy time series captures patterns from past data. In this study, the data used for forecasting is monthly selling data based on the last three years. This research uses five types of products that are sold the most in the boutique. The trial result of forecasting selling products during January 2017-December 2019 that uses the average-based fuzzy time series model show the result of the next month following data: the average error for the robe is 4.81%, 11.21% for the tunic, 8.78% for pashmina, 18.35% for khimar, and 9.72% for jeans. These results show that forecasting selling products in an Ainaya Boutique using the average-based fuzzy time series model has a high accuracy level.

show abstract

Fuzzy model translation for time series data in the extent of median error and its application

Cited by 3 publications

References 6 publications

Fuzzy Vector Autoregression for Forecasting Farmer Exchange Rate in Central Java Province

Fuzzy Vector Autoregression for Forecasting Farmer Exchange Rate in Central Java Province

Fuzzy Model Optimization for Time Series Data Using a Translation in the Extent of Mean Error

Implementation of Average-Based Fuzzy Time Series Model in Forecasting Product Selling at Ainaya Boutique

Contact Info

Product

Resources

About