Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

Pant, Manish; Kumar, Sanjay

doi:10.1007/s41066-021-00300-3

Cited by 27 publications

(6 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SVM [15] is a machine learning algorithm that is employed for both regressions and classifications depending upon the enigmas. In Linear SVM, features are linearly arranged [16] that can utilize a simple straight line to implement SVM in this case. The formula for obtaining hyperplane in this case is as shown in "Eq.…”

Section: Support Vector Machinementioning

confidence: 99%

Experimental Performance Analysis of Machine Learning Algorithms

Khekare

Turukmane

Dhule

et al. 2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

View full text Add to dashboard Cite

Machine Learning models and algorithms have become quite common these days. Deep Learning and Machine Learning algorithms are utilized in various projects, and now, it has opened the door to several opportunities in various fields of research and business. However, identifying the appropriate algorithm for a particular program has always been an enigma, and that necessitates to be solved ere the development of any machine learning system. Let’s take the example of the Stock Price Prediction system, it is used to identify the future asset prediction of a industry or other financial aspects traded on a related transaction. Now, it is a daunting task to find the right algorithm or model for such a purpose that can predict accurate values. There are several other systems such as recommendation systems, sales prediction of a mega-store, or predicting what are the chances of a driver meeting an accident based on his past records and the road they’ve taken. These problem statements require to be built using the most suitable algorithm and identifying them is a necessary task. This is what the system does, it compares a set of machine learning algorithms while determining the appropriate algorithm for the selected predictive system using the required data sets. The objective is to develop an interface that can be used to display the result matrix of different machine learning algorithms after being exposed to different datasets with different features. Besides that, one can determine the most suitable (or optimal) models for their operations, using these fundamentals. For experimental performance analysis several technologies and tools are used including Python, Django, Jupyter Notebook, Machine Learning, Data Science methodologies, etc. The comparative performance analysis of best known five time series forecasting machine learning algorithms viz. linear regression, K – nearest neighbor, Auto ARIMA, Prophet, and Support Vector Machine is done. Stock market, earth and sales forecasting data is used for analysis.

show abstract

Section: Support Vector Machinementioning

confidence: 99%

Experimental Performance Analysis of Machine Learning Algorithms

Khekare

Turukmane

Dhule

et al. 2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

View full text Add to dashboard Cite

show abstract

“…Furthermore, fuzzy logic has been applied to various aspects of economic forecasting, including stock market predictions, consumer behavior analysis, and macroeconomic modeling. For instance, studies by Ecer et al [ 30 ] and Pant et al [ 31 ] have used fuzzy logic to predict stock prices based on a range of economic indicators and market sentiment analysis. These studies have shown that fuzzy logic can capture the non-linear and uncertain nature of financial markets, leading to more accurate predictions compared to traditional statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Impact of economic indicators on rice production: A machine learning approach in Sri Lanka

Kularathne,

Rathnayake,

Herath

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models using machine learning techniques. The dataset, spanning from 1960 to 2020, includes key economic variables such as GDP, inflation rate, manufacturing output, population, population growth rate, imports, arable land area, military expenditure, and rice production. The study’s findings reveal the significant influence of economic factors on rice production in Sri Lanka. Machine learning models, including Linear Regression, Support Vector Machines, Ensemble methods, and Gaussian Process Regression, demonstrate strong predictive accuracy in forecasting rice production based on economic indicators. These results underscore the importance of economic indicators in shaping rice production outcomes and highlight the potential of machine learning in predicting agricultural trends. The study suggests avenues for future research, such as exploring regional variations and refining models based on ongoing data collection.

show abstract

“…Pant & Kumar [55] developed PSO and computational algorithm based weighted FTS forecasting method. Pant & Kumar [56] developed a hybrid method for FTS forecasting method using PSO, hesitant fuzzy set and support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic fuzzy set and particle swarm optimization based computational method for fuzzy time series forecasting

Pant

Kumar

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Computational methods for time series forecasting have always an edge over conventional methods of forecasting due to their easy implementation and prominent characteristics of coping with large amount of time series data. Many computational methods for fuzzy time series (FTS) forecasting have been developed in past using fuzzy set, intuitionistic fuzzy set (IFS), and hesitant fuzzy set (HFS) for incorporating uncertainty, non-determinism, and hesitation in time series forecasting. Since probabilistic fuzzy set (PFS) incorporates both probabilistic and non-probabilistic uncertainties simultaneously, we have proposed PFS and particle swarm optimization (PSO) based computational method for FTS forecasting. First, we have developed a PFS based computational method for FTS forecasting and then it is integrated with PSO to enhance the accuracy in forecasted outputs. Unlike other PSO based for FTS forecasting method, PSO is used to optimize both number of partitions and length of intervals. Three diversified time series data of enrolments of the University of Alabama, market price of State Bank of India (SBI) share at Bombay stock exchange (BSE) India, and death cases due to COVID-19 in India are used to compare the performance of PFS based computational method of FTS forecasting before and after its integration with PSO in terms of root mean square error (RMSE). After integration of PFS based computational method with PSO, accuracy in the forecasted outputs is increased significantly and its performance is found better than many other existing FTS forecasting methods. Goodness of the proposed FTS forecasting method is also tested using tracking signal and Willmott index.

show abstract

Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

Cited by 27 publications

References 71 publications

Experimental Performance Analysis of Machine Learning Algorithms

Experimental Performance Analysis of Machine Learning Algorithms

Impact of economic indicators on rice production: A machine learning approach in Sri Lanka

Probabilistic fuzzy set and particle swarm optimization based computational method for fuzzy time series forecasting

Contact Info

Product

Resources

About