A Comparative Study of Fuzzy Logic Regression and ARIMA Models for Prediction of Gram Production

Iqbal, Shafqat; Zhang, Chongqi; Arif, Muhammad; Dicu, Anca Mihaela

doi:10.1007/978-3-030-52190-5_21

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…It is a suitable model to be utilized in forecasting the linguistic quantities. Furthermore, recently, Iqbal et al (2018 and developed robust fuzzy time series models to generate forecasts with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method

Hanif

Mustafa

Iqbal

et al. 2022

JCCE

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A time series is a sequence of elements with numerical data in sequential order and having regular intervals. Time series are used in statistics, enrollments, signal processing, econometrics, mathematical finance, and weather forecasting. It helps us to forecast and predict the time series data in different domains. There are many methods to forecast the enrollments in literature which have large applications and are presented in the field of statistics and econometrics. One of the robust methods, we used in our research, is moving average. It helps to forecast and predict the data whenever the fuzziness occurs in time series data, which is not appropriate in crisp time series forecasting. To get rid of this problem, the fuzzy interval partitioning method proved to be more appropriate to generate accurate results. This research will focus to overcome the failure of the crisp method and to show the use of a fuzzy interval partitioning method. The fuzzy interval partitioning method is different from another interval partition schemes because it specifies the linguistic values rather than numerical values. It is also used to deal with uncertain conditions. So, fuzzy interval partitioning improves data utilization and also calculates the higher predicted accuracy rate. Besides this research, we use a quantitative method and a fuzzy moving average with the interval partitioning method. Then we compare the efficiency of moving average model and moving average with fuzzy interval partitioning method for forecasting the enrollments.

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Section: Introductionmentioning

confidence: 99%

A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method

Hanif

Mustafa

Iqbal

et al. 2022

JCCE

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Hybrid Predictive Modeling for Automotive After-Sales Pricing: Integrating BiLSTM-Attention and Fuzzy Logic

Amellal,

Ech-charrat

2024

Lecture Notes in Business Information Processing

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Development of an integrated blended learning model and its performance prediction on students’ learning using Bayesian network

Lakho

Jalbani

Memon

et al. 2022

IFS

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With the spread of the COVID-19 pandemic, the importance of online learning has grown up worldwide and many higher education institutions used this mode of learning to save the timings of students. Just Online learning does not fulfill all the learning requirements of undergraduate students, therefore, there is a need for the blended learning (BL) method to be adopted in higher educational institutes for the enhancement of students’ learning outcomes. This research paper focuses on the development of an integrated blended learning model and the performance of the model on students’ learning has been predicted using a Bayesian network (BN) classifier. The proposed model is based on the medium impact blend of the Rotation model and the Enriched Virtual Model and applied to undergraduate computing students. The Data Structures and Algorithms course is targeted for the prediction of students’ performance. The findings of the proposed Integrated BL model show that when students properly attend the classroom lectures followed by their associated lab practical in the Rotation Model and follow the online learning activities in the Enriched Virtual Model properly, then their learning outcomes may be increased as predicted using BN method. The proposed model also reports an overall accuracy of 88.5% on the collected data.

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A Comparative Study of Fuzzy Logic Regression and ARIMA Models for Prediction of Gram Production

Cited by 3 publications

References 13 publications

A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method

A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method

Hybrid Predictive Modeling for Automotive After-Sales Pricing: Integrating BiLSTM-Attention and Fuzzy Logic

Development of an integrated blended learning model and its performance prediction on students’ learning using Bayesian network

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