Particle swarm optimization and intuitionistic fuzzy set-based novel method for fuzzy time series forecasting

Pant, Manish; Kumar, Sanjay

doi:10.1007/s41066-021-00265-3

Cited by 54 publications

(13 citation statements)

References 62 publications

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“…It is observed from Table 4 and Table 8 that PFS based computational method of FTS forecasting outperforms than the methods of [33,34,36,54,58,59,60]in forecasting of enrolments of the University of Alabama in terms of RMSE. However, performance of PFS based computational method is found substandard than methods of Bas et al [60] and Pant & Kumar [41], but high value of TS associated with these methods raise doubt in forecasting of enrollments of the University of Alabama.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 93%

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

Pant

Kumar

2023

Preprint

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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.

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Section: Comparative Analysis and Discussionmentioning

confidence: 93%

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

Pant

Kumar

2023

Preprint

Self Cite

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“…The suggested model (GBCFTS-PSO) is used in this subsection to forecast enrolments with annual observations [4] . The results of five forecasting models in works [41][42][43][44]50] are chosen for comparison to demonstrate the performance of the suggested forecasting model based on first-order FTS under varied intervals. Table 8 and Figure 4 show the forecasted values and the forecasting accuracy between our suggested model and comparing models.…”

Section: Forecasting the Enrolments Of University Of Alabamamentioning

confidence: 99%

“…Another approach based on fuzzy time series, some of the authors in works [41,42] introduced intuitionistic fuzzy time series (IFTS) and established an IFTS forecasting model to forecast the University of Alabama enrolment and State Bank of India (SBI) market share price on the Bombay stock exchange (BSE). The authors in research work [43,44] introduced hesitant probabilistic fuzzy sets in time series forecasting to address the issues of non-stochastic non-determinism and apply for forecasting the enrolments of the University of Alabama and the share market prizes of the State Bank of India.…”

Section: Introductionmentioning

confidence: 99%

Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

Luong

Thanh

Tính

et al. 2023

j. of Comput. sci. res.

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In recent years, a wide variety of fuzzy time series (FTS) forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world sources. However, the accuracy of a model is problem-specific and varies across data sets. But a model’s precision varies between different data sets and depends on the situation at hand. Even though many models assert that they are better than statistics and a single machine learning-based model, increasing forecasting accuracy is still a challenging task. In the fuzzy time series models, the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model’s forecasting abilities. This study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique (GBC) and particle swarm optimization (PSO) for adjusting interval lengths in the universe of discourse (UoD). The suggested model’s forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of Alabama. For all orders of fuzzy relationships, the suggested model outperforms its counterparts in terms of forecasting accuracy.

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“…The modelling of time series is extremely important to make good inferences about the future, which provides a strong theoretical foundation for information processing and decision analysis, which has been an important domain of research (Pant & Kumar, 2022). Analysis and prediction for time series provide a better method of decision support (Hu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A nonlinear time-series prediction methodology based on neural networks and tracking signals

Bianchesi

Matta

Streitenberger

et al. 2022

Prod.

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Paper aims: This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series.Originality: This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient.Research method: Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings:The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice:This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

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Particle swarm optimization and intuitionistic fuzzy set-based novel method for fuzzy time series forecasting

Cited by 54 publications

References 62 publications

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

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

Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

A nonlinear time-series prediction methodology based on neural networks and tracking signals

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