A Trend based Forecasting Model Using Fuzzy Time Series and PSO Algorithm

Huang, Yi; Hsieh, Wen-Kuei; Shih-Wei,; Lin, Kun-You

doi:10.1109/iccce48422.2019.9010899

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…This method was used to predict the unknown future value proven to reduce the means squared error (RMSE). This also improves the accuracy as compared to the other models based on fuzzy time series ( Huang, Hsieh & Lin, 2019 ).…”

Section: Literature Reviewmentioning

confidence: 81%

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

Hartomo

Nataliani

2021

PeerJ Computer Science

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This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

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Section: Literature Reviewmentioning

confidence: 81%

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

Hartomo

Nataliani

2021

PeerJ Computer Science

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“…In recent years various forecasting models are used to improve the accuracy of forecasting using evolutionary methods and fuzzy approaches. In [13] a hybrid model is proposed with fuzzy time series and particle swarm optimization algorithm to inaugurate predicting model. In [14] a model is been proposed for unprocessed raw data called the Functional Link Artificial Neural Network (FLANN) model, it also specifies the effectiveness of the model for time series seasonal forecasting.…”

Section: Related Workmentioning

confidence: 99%

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

Ashwini¹,

Nagaveni²,

Singh³

2022

Intelligent Automation &Amp; Soft Computing

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“…Zhang et al [29] used time series clustering and multiple linear regressions to present FTS forecasting model. Several authors proposed an improved version of forecasted accuracy by combining PSO with FTS [30][31][32][33][34]. Recently, Tinh [35] has developed hybrid FTS forecasting model based on the combination of PSO and FCM techniques together.…”

Section: Introductionmentioning

confidence: 99%

A Study for Plausible Third Wave of COVID-19 in India through Fuzzy Time Series Modelling Based on Particle Swarm Optimization and Fuzzy c-Means

Kumar

2022

Mathematical Problems in Engineering

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The outbreak of COVID-19 has become a global pandemic as announced by World Health Organisation. As India has already met the two waves, named first and second wave, it is assumed that COVID-19 will again strike in India in the form of third wave. The peak during the upcoming third wave and determination of the approximated maximum number of COVID-19 infected cases and deaths at a particular day becomes crucial for India. To determine the peak of infectious curve, this article proposed a hybrid fuzzy time series forecasting model based on particle swarm optimization and fuzzy c-mean technique, named as fuzzy time series particle swarm optimization extended fuzzy c-mean technique. The proposed model works in two phases. In phase-I, particle swarm optimization extended fuzzy c-mean method is used to form initial intervals with the help of centroids, while in phase-II, these intervals are updated to form subintervals. In the present article, a fitness function is developed for particle swarm optimization to increase its convergence speed and basic fuzzy c-mean is extended by using an exponential function to tolerate the effect of outliers, named as extended fuzzy c-mean technique. The effectiveness of the proposed model has been tested based on mean square error and root mean square error on first and second wave COVID-19 data, and the obtained results are very close to the existing data of COVID-19 with less error rate. Thus, the proposed model is suitable to forecast a better approximation value of COVID-19 infected cases and deaths in India during the upcoming third wave. This study demonstrates that third wave of COVID-19 could occur in India, while also illustrating that it is unlikely for any such resurgence to be as large as the second wave. The proposed model predicts that the peak of third wave will occur approximately after 40–70 days from the mid of December. Furthermore, the impact of vaccination on infected cases and deaths during the upcoming third wave in India is also studied. With the implementation of the vaccine on the Indian people, the peak of COVID-19 infected during third wave will be shifted in forward direction. On the basis of the proposed model, government authorities will be enabling to know expected required resources such as hospital patient beds, ICU beds, and oxygen concentrators during the upcoming outspread of COVID-19 like disease in future.

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A Trend based Forecasting Model Using Fuzzy Time Series and PSO Algorithm

Cited by 4 publications

References 8 publications

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

A Study for Plausible Third Wave of COVID-19 in India through Fuzzy Time Series Modelling Based on Particle Swarm Optimization and Fuzzy c-Means

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